Advertisement
| aspect based sentiment analysis: Sentiment Analysis in Social Networks Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu, 2016-10-06 The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network analysis - Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network mining - Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics |
| aspect based sentiment analysis: Sentiment Analysis Bing Liu, 2020-10-15 Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis. |
| aspect based sentiment analysis: Deep Learning-Based Approaches for Sentiment Analysis Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik, 2020-01-24 This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field. |
| aspect based sentiment analysis: A Practical Guide to Sentiment Analysis Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay, Antonio Feraco, 2017-04-07 Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers’ sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well. |
| aspect based sentiment analysis: Sentiment Analysis and Opinion Mining Bing Liu, 2012 Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography |
| aspect based sentiment analysis: Sentic Computing Erik Cambria, Amir Hussain, 2012-07-28 In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. |
| aspect based sentiment analysis: Recent Advances in NLP: The Case of Arabic Language Mohamed Abd Elaziz, Mohammed A. A. Al-qaness, Ahmed A. Ewees, Abdelghani Dahou, 2019-11-29 In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence. The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources. This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas. |
| aspect based sentiment analysis: Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance Rana, Dipti P., Mehta, Rupa G., 2021-06-04 Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches. |
| aspect based sentiment analysis: Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019) A. Pasumpon Pandian, Ram Palanisamy, Klimis Ntalianis, 2020-03-04 This book presents the proceedings of the International Conference on Computing Networks, Big Data and IoT [ICCBI 2019], held on December 19–20, 2019 at the Vaigai College of Engineering, Madurai, India. Recent years have witnessed the intertwining development of the Internet of Things and big data, which are increasingly deployed in computer network architecture. As society becomes smarter, it is critical to replace the traditional technologies with modern ICT architectures. In this context, the Internet of Things connects smart objects through the Internet and as a result generates big data. This has led to new computing facilities being developed to derive intelligent decisions in the big data environment. The book covers a variety of topics, including information management, mobile computing and applications, emerging IoT applications, distributed communication networks, cloud computing, and healthcare big data. It also discusses security and privacy issues, network intrusion detection, cryptography, 5G/6G networks, social network analysis, artificial intelligence, human–machine interaction, smart home and smart city applications. |
| aspect based sentiment analysis: Information and Communication Technology for Sustainable Development Milan Tuba, Shyam Akashe, Amit Joshi, 2019-06-26 The book proposes new technologies and discusses future solutions for ICT design infrastructures, and includes high-quality submissions presented at the Third International Conference on ICT for Sustainable Development (ICT4SD 2018), held in Goa, India on 30–31 August 2018. The conference stimulated cutting-edge research discussions among pioneering researchers, scientists, industrial engineers, and students from all around the world. Bringing together experts from different countries, the book focuses on innovative issues at an international level. |
| aspect based sentiment analysis: 2021 7th International Conference on Web Research (ICWR) , 2021 |
| aspect based sentiment analysis: Proceedings of Second International Conference on Computing, Communications, and Cyber-Security Pradeep Kumar Singh, Sławomir T. Wierzchoń, Sudeep Tanwar, Maria Ganzha, Joel J. P. C. Rodrigues, 2021-05-24 This book features selected research papers presented at the Second International Conference on Computing, Communications, and Cyber-Security (IC4S 2020), organized in Krishna Engineering College (KEC), Ghaziabad, India, along with Academic Associates; Southern Federal University, Russia; IAC Educational, India; and ITS Mohan Nagar, Ghaziabad, India during 3–4 October 2020. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues. |
| aspect based sentiment analysis: Social Internet of Things Alessandro Soro, Margot Brereton, Paul Roe, 2018-07-20 The aim of this book is to stimulate research on the topic of the Social Internet of Things, and explore how Internet of Things architectures, tools, and services can be conceptualized and developed so as to reveal, amplify and inspire the capacities of people, including the socialization or collaborations that happen through or around smart objects and smart environments. From new ways of negotiating privacy, to the consequences of increased automation, the Internet of Things poses new challenges and opens up new questions that often go beyond the technology itself, and rather focus on how the technology will become embedded in our future communities, families, practices, and environment, and how these will change in turn. |
| aspect based sentiment analysis: Proceedings of International Joint Conference on Advances in Computational Intelligence Mohammad Shorif Uddin, Jagdish Chand Bansal, 2021-05-17 This book gathers outstanding research papers presented at the International Joint Conference on Advances in Computational Intelligence (IJCACI 2020), organized by Daffodil International University (DIU) and Jahangirnagar University (JU) in Bangladesh and South Asian University (SAU) in India. These proceedings present novel contributions in the areas of computational intelligence and offer valuable reference material for advanced research. The topics covered include collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal and natural language processing. |
| aspect based sentiment analysis: Machine Learning Techniques and Analytics for Cloud Security Rajdeep Chakraborty, Anupam Ghosh, Jyotsna Kumar Mandal, 2021-11-30 MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography. |
| aspect based sentiment analysis: Cyber Security and Computer Science Touhid Bhuiyan, Md. Mostafijur Rahman, Md. Asraf Ali, 2020-07-29 This book constitutes the refereed post-conference proceedings of the Second International Conference on Cyber Security and Computer Science, ICONCS 2020, held in Dhaka, Bangladesh, in February 2020. The 58 full papers were carefully reviewed and selected from 133 submissions. The papers detail new ideas, inventions, and application experiences to cyber security systems. They are organized in topical sections on optimization problems; image steganography and risk analysis on web applications; machine learning in disease diagnosis and monitoring; computer vision and image processing in health care; text and speech processing; machine learning in health care; blockchain applications; computer vision and image processing in health care; malware analysis; computer vision; future technology applications; computer networks; machine learning on imbalanced data; computer security; Bangla language processing. |
| aspect based sentiment analysis: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| aspect based sentiment analysis: Inventive Computation and Information Technologies S. Smys, Valentina Emilia Balas, Khaled A. Kamel, Pavel Lafata, 2021-03-27 This book is a collection of best selected papers presented at the International Conference on Inventive Computation and Information Technologies (ICICIT 2020), organized during 24–25 September 2020. The book includes papers in the research area of information sciences and communication engineering. The book presents novel and innovative research results in theory, methodology and applications of communication engineering and information technologies. |
| aspect based sentiment analysis: Data Preprocessing in Data Mining Salvador García, Julián Luengo, Francisco Herrera, 2014-08-30 Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering. |
| aspect based sentiment analysis: Handbook of Natural Language Processing Nitin Indurkhya, Fred J. Damerau, 2010-02-22 The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater |
| aspect based sentiment analysis: Intelligent Data Communication Technologies and Internet of Things Jude Hemanth, Robert Bestak, Joy Iong-Zong Chen, 2021-02-12 This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application domains. The proliferation of various mobile and wireless communication networks has paved way to foster a high demand for intelligent data processing and communication technologies. The potential of data in wireless mobile networks is enormous, and it constitutes to improve the communication capabilities profoundly. As the networking and communication applications are becoming more intensive, the management of data resources and its flow between various storage and computing resources are posing significant research challenges to both ICT and data science community. The general scope of this book covers the design, architecture, modeling, software, infrastructure and applications of intelligent communication architectures and systems for big data or data-intensive applications. In particular, this book reports the novel and recent research works on big data, mobile and wireless networks, artificial intelligence, machine learning, social network mining, intelligent computing technologies, image analysis, robotics and autonomous systems, data security and privacy. |
| aspect based sentiment analysis: My Antonia Willa Cather, 2024-01-02 A haunting tribute to the heroic pioneers who shaped the American Midwest This powerful novel by Willa Cather is considered to be one of her finest works and placed Cather in the forefront of women novelists. It tells the stories of several immigrant families who start new lives in America in rural Nebraska. This powerful tribute to the quiet heroism of those whose struggles and triumphs shaped the American Midwest highlights the role of women pioneers, in particular. Written in the style of a memoir penned by Antonia’s tutor and friend, the book depicts one of the most memorable heroines in American literature, the spirited eldest daughter of a Czech immigrant family, whose calm, quite strength and robust spirit helped her survive the hardships and loneliness of life on the Nebraska prairie. The two form an enduring bond and through his chronicle, we watch Antonia shape the land while dealing with poverty, treachery, and tragedy. “No romantic novel ever written in America...is one half so beautiful as My Ántonia.” -H. L. Mencken Willa Cather (1873–1947) was an American writer best known for her novels of the Plains and for One of Ours, a novel set in World War I, for which she was awarded the Pulitzer Prize in 1923. She was elected a fellow of the American Academy of Arts and Sciences in 1943 and received the gold medal for fiction from the National Institute of Arts and Letters in 1944, an award given once a decade for an author's total accomplishments. By the time of her death she had written twelve novels, five books of short stories, and a collection of poetry. |
| aspect based sentiment analysis: Multi-Modal Sentiment Analysis Hua Xu, 2023-11-26 The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields. |
| aspect based sentiment analysis: Letter from Birmingham Jail Martin Luther King, 2025-01-14 A beautiful commemorative edition of Dr. Martin Luther King's essay Letter from Birmingham Jail, part of Dr. King's archives published exclusively by HarperCollins. With an afterword by Reginald Dwayne Betts On April 16, 1923, Dr. Martin Luther King Jr., responded to an open letter written and published by eight white clergyman admonishing the civil rights demonstrations happening in Birmingham, Alabama. Dr. King drafted his seminal response on scraps of paper smuggled into jail. King criticizes his detractors for caring more about order than justice, defends nonviolent protests, and argues for the moral responsibility to obey just laws while disobeying unjust ones. Letter from Birmingham Jail proclaims a message - confronting any injustice is an acceptable and righteous reason for civil disobedience. This beautifully designed edition presents Dr. King's speech in its entirety, paying tribute to this extraordinary leader and his immeasurable contribution, and inspiring a new generation of activists dedicated to carrying on the fight for justice and equality. |
| aspect based sentiment analysis: Natural Language Processing and Information Systems Elisabeth Métais, Farid Meziane, Helmut Horacek, Philipp Cimiano, 2020-06-18 This book constitutes the refereed proceedings of the 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, held in Saarbrücken, Germany, in June 2020.* The 15 full papers and 10 short papers were carefully reviewed and selected from 68 submissions. The papers are organized in the following topical sections: semantic analysis; question answering and answer generation; classification; sentiment analysis; personality, affect and emotion; retrieval, conversational agents and multimodal analysis. *The conference was held virtually due to the COVID-19 pandemic. |
| aspect based sentiment analysis: Natural Language Processing and Information Systems Elisabeth Métais, Farid Meziane, Mohamad Sararee, Vijayan Sugumaran, Sunil Vadera, 2013-06-06 This book constitutes the refereed proceedings of the 18th International Conference on Applications of Natural Language to Information Systems, held in Salford, UK, in June 2013. The 21 long papers, 15 short papers and 17 poster papers presented in this volume were carefully reviewed and selected from 80 submissions. The papers cover the following topics: requirements engineering, question answering systems, named entity recognition, sentiment analysis and mining, forensic computing, semantic web, and information search. |
| aspect based sentiment analysis: 2021 International Joint Conference on Neural Networks (IJCNN) IEEE Staff, 2021-07-18 JCNN is the premier international conference on neural networks theory, analysis, and a wide range of applications IJCNN 2021 is a truly interdisciplinary event with a broad range of contributions on recent advances in neural networks, including neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, bioinformatics and biomedicine, and engineering applications |
| aspect based sentiment analysis: Advances in Knowledge Discovery and Data Mining Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty, 2021-06-18 The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data. |
| aspect based sentiment analysis: Lord of the Flies William Golding, 2012-09-20 A plane crashes on a desert island and the only survivors, a group of schoolboys, assemble on the beach and wait to be rescued. By day they inhabit a land of bright fantastic birds and dark blue seas, but at night their dreams are haunted by the image of a terrifying beast. As the boys' delicate sense of order fades, so their childish dreams are transformed into something more primitive, and their behaviour starts to take on a murderous, savage significance. First published in 1954, Lord of the Flies is one of the most celebrated and widely read of modern classics. Now fully revised and updated, this educational edition includes chapter summaries, comprehension questions, discussion points, classroom activities, a biographical profile of Golding, historical context relevant to the novel and an essay on Lord of the Flies by William Golding entitled 'Fable'. Aimed at Key Stage 3 and 4 students, it also includes a section on literary theory for advanced or A-level students. The educational edition encourages original and independent thinking while guiding the student through the text - ideal for use in the classroom and at home. |
| aspect based sentiment analysis: Neural Information Processing Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King, 2020-11-19 The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.* For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 papers included in this volume set were organized in topical sections as follows: data mining; healthcare analytics-improving healthcare outcomes using big data analytics; human activity recognition; image processing and computer vision; natural language processing; recommender systems; the 13th international workshop on artificial intelligence and cybersecurity; computational intelligence; machine learning; neural network models; robotics and control; and time series analysis. * The conference was held virtually due to the COVID-19 pandemic. |
| aspect based sentiment analysis: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media Brij Gupta, Ahmed A. Abd El-Latif, Dragan Perakovic, 2021 This book explores the key concepts of data mining and utilizing them on online social media platforms, offering valuable insight into data mining approaches for big data and sentiment analysis in online social media and covering many important security and other aspects and current trends-- |
| aspect based sentiment analysis: 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) IEEE Staff, 2021-10-28 This conference s topics are Computer Science, Artificial intelligence and all topics related with recent technology in Computer and Society 5 0 |
| aspect based sentiment analysis: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) IEEE Staff, 2019-11-04 ICTAI 2019 The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading Conference of AI in the Computer Society providing a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies The conference facilitates the cross fertilization of AI ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications The ICTAI encompasses all technical aspects of specifying, developing and evaluating the theoretical underpinnings and applied mechanisms of the AI based components of computer tools (i e algorithms, architectures or languages) |
| aspect based sentiment analysis: Sentiment Analysis Bing Liu, 2020-10-15 A comprehensive introduction to computational analysis of sentiments, opinions, emotions, and moods. Now including deep learning methods. |
| aspect based sentiment analysis: Opinions, Sentiment, and Emotion in Text Bing Liu, 2015-06-04 This book gives a comprehensive introduction to all the core areas and many emerging themes of sentiment analysis. |
| aspect based sentiment analysis: Intelligent Information and Database Systems Ngoc-Thanh Nguyen, Bogdan Trawiński, Hamido Fujita, Tzung-Pei Hong, 2016-03-08 The two-volume proceedings of the ACIIDS 2016 conference, LNAI 9621 + 9622, constitutes the refereed proceedings of the 8th Asian Conference on Intelligent Information and Database Systems, held in Da Nang, Vietnam, in March 2016. The total of 153 full papers accepted for publication in these proceedings was carefully reviewed and selected from 392 submissions. They were organized in topical sections named: knowledge engineering and semantic Web; social networks and recommender systems; text processing and information retrieval; database systems and software engineering; intelligent information systems; decision support and control systems; machine learning and data mining; computer vision techniques; intelligent big data exploitation; cloud and network computing; multiple model approach to machine learning; advanced data mining techniques and applications; computational intelligence in data mining for complex problems; collective intelligence for service innovation, technology opportunity, e-learning, and fuzzy intelligent systems; analysis for image, video and motion data in life sciences; real world applications in engineering and technology; ontology-based software development; intelligent and context systems; modeling and optimization techniques in information systems, database systems and industrial systems; smart pattern processing for sports; and intelligent services for smart cities. |
| aspect based sentiment analysis: Prominent Feature Extraction for Sentiment Analysis Basant Agarwal, Namita Mittal, 2015-12-14 The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis. |
| aspect based sentiment analysis: Database Systems for Advanced Applications Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang, 2020-09-21 The 4 volume set LNCS 12112-12114 constitutes the papers of the 25th International Conference on Database Systems for Advanced Applications which will be held online in September 2020. The 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions. The conference program presents the state-of-the-art R&D activities in database systems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry. |
| aspect based sentiment analysis: Data Engineering and Applications Jitendra Agrawal, |
| aspect based sentiment analysis: ECAI 2023 K. Gal, A. Nowé, G.J. Nalepa, 2023-10-18 Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field. |
| aspect-based sentiment analysis: Sentiment Analysis in Social Networks Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu, 2016-10-06 The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network analysis - Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network mining - Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics |
| aspect-based sentiment analysis: Sentiment Analysis Bing Liu, 2020-10-15 Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis. |
| aspect-based sentiment analysis: Deep Learning-Based Approaches for Sentiment Analysis Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik, 2020-01-24 This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field. |
| aspect-based sentiment analysis: A Practical Guide to Sentiment Analysis Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay, Antonio Feraco, 2017-04-07 Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers’ sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well. |
| aspect-based sentiment analysis: Sentiment Analysis and Opinion Mining Bing Liu, 2012 Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography |
| aspect-based sentiment analysis: Sentic Computing Erik Cambria, Amir Hussain, 2012-07-28 In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. |
| aspect-based sentiment analysis: Recent Advances in NLP: The Case of Arabic Language Mohamed Abd Elaziz, Mohammed A. A. Al-qaness, Ahmed A. Ewees, Abdelghani Dahou, 2019-11-29 In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence. The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources. This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas. |
| aspect-based sentiment analysis: Advances in Computer Science and Ubiquitous Computing James J. Park, Vincenzo Loia, Gangman Yi, Yunsick Sung, 2017-12-19 This book presents the combined proceedings of the 12th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE 2017) and the 9th International Conference on Computer Science and its Applications (CSA2017), both held in Taichung, Taiwan, December 18 - 20, 2017. The aim of these two meetings was to promote discussion and interaction among academics, researchers and professionals in the field of ubiquitous computing technologies. These proceedings reflect the state of the art in the development of computational methods, involving theory, algorithms, numerical simulation, error and uncertainty analysis and novel applications of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing. James J. (Jong Hyuk) Park received Ph.D. degrees in Graduate School of Information Security from Korea University, Korea and Graduate School of Human Sciences from Waseda University, Japan. From December, 2002 to July, 2007, Dr. Park had been a research scientist of R&D Institute, Hanwha S&C Co., Ltd., Korea. From September, 2007 to August, 2009, He had been a professor at the Department of Computer Science and Engineering, Kyungnam University, Korea. He is now a professor at the Department of Computer Science and Engineering and Department of Interdisciplinary Bio IT Materials, Seoul National University of Science and Technology (SeoulTech), Korea. Dr. Park has published about 200 research papers in international journals and conferences. He has been serving as chair, program committee, or organizing committee chair for many international conferences and workshops. He is a steering chair of international conferences – MUE, FutureTech, CSA, CUTE, UCAWSN, World IT Congress-Jeju. He is editor-in-chief of Human-centric Computing and Information Sciences (HCIS) by Springer, The Journal of Information Processing Systems (JIPS) by KIPS, and Journal of Convergence (JoC) by KIPS CSWRG. He is Associate Editor / Editor of 14 international journals including JoS, JNCA, SCN, CJ, and so on. In addition, he has been serving as a Guest Editor for international journals by some publishers: Springer, Elsevier, John Wiley, Oxford Univ. press, Emerald, Inderscience, MDPI. He got the best paper awards from ISA-08 and ITCS-11 conferences and the outstanding leadership awards from IEEE HPCC-09, ICA3PP-10, IEE ISPA-11, PDCAT-11, IEEE AINA-15. Furthermore, he got the outstanding research awards from the SeoulTech, 2014. His research interests include IoT, Human-centric Ubiquitous Computing, Information Security, Digital Forensics, Vehicular Cloud Computing, Multimedia Computing, etc. He is a member of the IEEE, IEEE Computer Society, KIPS, and KMMS. Vincenzo Loia (BS ‘85, MS ‘87, PhD ‘89) is Full Professor of Computer Science. His research interests include Intelligent Agents, Ambient intelligence, Computational Intelligence. Currently he is Founder & Editor-in-chief of “Ambient Intelligence and Humanized Computing”, and Co-Editor-in-Chief of “Softcomputing”, Springer-Verlag. He is Chair of the Task Forces “Intelligent Agents” and “Ambient Intelligence” IEEE CIS ETTC. He has been Chair the Emergent Technical Committe Emergent Technology, IEEE CIS Society and Vice-Chair of Intelligent Systems Applications Technical Committee. He has been author of more than 200 scientific works, Editor/co-editor of 4 Books, 64 journal papers, 25 book chapters, and 100 conference papers. He is Senior member of the IEEE, Associate Editor of IEEE Transactions on Industrial Informatics, and Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems. Many times reviewers for national and international projects, Dr. Loia is active in the research domain of agents, ambient intelligence, computational intelligence, smartgrids, distributed platform for enrich added value. Gangman Yi in Computer Sciences at Texas A&M University, USA in 2007, and doctorate in Computer Sciences at Texas A&M University, USA in 2011. In May 2011, he joined System S/W group in Samsung Electronics, Suwon, Korea. He joined the Department of Computer Science & Engineering, Gangneung-Wonju National University, Korea, since March 2012. Dr. Yi has been researched in an interdisciplinary field of researches. His research focuses especially on the development of computational methods to improve understanding of biological systems and its big data. Dr. Yi actively serves as a managing editor and reviewer for international journals, and chair of international conferences and workshops. Yunsick Sung received his B.S. degree in division of electrical and computer engineering from Pusan National University, Busan, Korea, in 2004, his M.S. degree in computer engineering from Dongguk University, Seoul, Korea, in 2006, and his Ph.D. degree in game engineering from Dongguk University, Seoul, Korea, in 2012. He was employed as a member of the researcher at Samsung Electronics between 2006 and 2009. He was the plural professor at Shinheung College in 2009 and at Dongguk University in 2010. His main research interests are many topics in brain-computer Interface, programming by demonstration, ubiquitous computing and reinforcement learning. His Journal Service Experiences is Associate Editor at Human-centric Computing and Information Sciences, Springer (2015- Current). |
| aspect-based sentiment analysis: Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance Rana, Dipti P., Mehta, Rupa G., 2021-06-04 Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches. |
| aspect-based sentiment analysis: Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019) A. Pasumpon Pandian, Ram Palanisamy, Klimis Ntalianis, 2020-03-04 This book presents the proceedings of the International Conference on Computing Networks, Big Data and IoT [ICCBI 2019], held on December 19–20, 2019 at the Vaigai College of Engineering, Madurai, India. Recent years have witnessed the intertwining development of the Internet of Things and big data, which are increasingly deployed in computer network architecture. As society becomes smarter, it is critical to replace the traditional technologies with modern ICT architectures. In this context, the Internet of Things connects smart objects through the Internet and as a result generates big data. This has led to new computing facilities being developed to derive intelligent decisions in the big data environment. The book covers a variety of topics, including information management, mobile computing and applications, emerging IoT applications, distributed communication networks, cloud computing, and healthcare big data. It also discusses security and privacy issues, network intrusion detection, cryptography, 5G/6G networks, social network analysis, artificial intelligence, human–machine interaction, smart home and smart city applications. |
| aspect-based sentiment analysis: Information and Communication Technology for Sustainable Development Milan Tuba, Shyam Akashe, Amit Joshi, 2019-06-26 The book proposes new technologies and discusses future solutions for ICT design infrastructures, and includes high-quality submissions presented at the Third International Conference on ICT for Sustainable Development (ICT4SD 2018), held in Goa, India on 30–31 August 2018. The conference stimulated cutting-edge research discussions among pioneering researchers, scientists, industrial engineers, and students from all around the world. Bringing together experts from different countries, the book focuses on innovative issues at an international level. |
| aspect-based sentiment analysis: 2021 7th International Conference on Web Research (ICWR) , 2021 |
| aspect-based sentiment analysis: Proceedings of Second International Conference on Computing, Communications, and Cyber-Security Pradeep Kumar Singh, Sławomir T. Wierzchoń, Sudeep Tanwar, Maria Ganzha, Joel J. P. C. Rodrigues, 2021-05-24 This book features selected research papers presented at the Second International Conference on Computing, Communications, and Cyber-Security (IC4S 2020), organized in Krishna Engineering College (KEC), Ghaziabad, India, along with Academic Associates; Southern Federal University, Russia; IAC Educational, India; and ITS Mohan Nagar, Ghaziabad, India during 3–4 October 2020. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues. |
| aspect-based sentiment analysis: Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence Trivedi, Shrawan Kumar, Dey, Shubhamoy, Kumar, Anil, Panda, Tapan Kumar, 2017-02-14 The development of business intelligence has enhanced the visualization of data to inform and facilitate business management and strategizing. By implementing effective data-driven techniques, this allows for advance reporting tools to cater to company-specific issues and challenges. The Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence is a key resource on the latest advancements in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners. |
| aspect-based sentiment analysis: Machine Learning Techniques and Analytics for Cloud Security Rajdeep Chakraborty, Anupam Ghosh, Jyotsna Kumar Mandal, 2021-11-30 MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography. |
| aspect-based sentiment analysis: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| aspect-based sentiment analysis: Data Preprocessing in Data Mining Salvador García, Julián Luengo, Francisco Herrera, 2014-08-30 Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering. |
| aspect-based sentiment analysis: Handbook of Natural Language Processing Nitin Indurkhya, Fred J. Damerau, 2010-02-22 The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater |
| aspect-based sentiment analysis: Big Data Management and the Internet of Things for Improved Health Systems Mishra, Brojo Kishore, Kumar, Raghvendra, 2018-01-19 Because of the increased access to high-speed Internet and smart phones, many patients have started to use mobile applications to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (IoT). Big Data Management and the Internet of Things for Improved Health Systems is a critical scholarly resource that examines the digital transformation of healthcare. Featuring coverage on a broad range of topics, such as brain computer interface, data reduction techniques, and risk factors, this book is geared towards academicians, practitioners, researchers, and students seeking research on health and well-being data. |
| aspect-based sentiment analysis: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program. |
| aspect-based sentiment analysis: Text Analytics with Python Dipanjan Sarkar, 2016-11-30 Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data |
| aspect-based sentiment analysis: Multi-Modal Sentiment Analysis Hua Xu, 2023-11-26 The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields. |
| aspect-based sentiment analysis: Information Retrieval Zhicheng Dou, Qiguang Miao, Wei Lu, Jiaxin Mao, Guang Jia, 2020-11-03 This book constitutes the refereed proceedings of the 26th China Conference on Information Retrieval, CCIR 2020, held in Xi'an, China, in August 2020.* The 12 full papers presented were carefully reviewed and selected from 102 submissions. The papers are organized in topical sections: search and recommendation, NLP for IR, and IR in finance. * Due to the COVID-19 pandemic the conference was held online supplemented with local on-site events. |
| aspect-based sentiment analysis: Letter from Birmingham Jail Martin Luther King, 2025-01-14 A beautiful commemorative edition of Dr. Martin Luther King's essay Letter from Birmingham Jail, part of Dr. King's archives published exclusively by HarperCollins. With an afterword by Reginald Dwayne Betts On April 16, 1923, Dr. Martin Luther King Jr., responded to an open letter written and published by eight white clergyman admonishing the civil rights demonstrations happening in Birmingham, Alabama. Dr. King drafted his seminal response on scraps of paper smuggled into jail. King criticizes his detractors for caring more about order than justice, defends nonviolent protests, and argues for the moral responsibility to obey just laws while disobeying unjust ones. Letter from Birmingham Jail proclaims a message - confronting any injustice is an acceptable and righteous reason for civil disobedience. This beautifully designed edition presents Dr. King's speech in its entirety, paying tribute to this extraordinary leader and his immeasurable contribution, and inspiring a new generation of activists dedicated to carrying on the fight for justice and equality. |
| aspect-based sentiment analysis: Advances in Knowledge Discovery and Data Mining Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty, 2021-06-18 The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data. |
| aspect-based sentiment analysis: 2021 International Joint Conference on Neural Networks (IJCNN) IEEE Staff, 2021-07-18 JCNN is the premier international conference on neural networks theory, analysis, and a wide range of applications IJCNN 2021 is a truly interdisciplinary event with a broad range of contributions on recent advances in neural networks, including neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, bioinformatics and biomedicine, and engineering applications |
| aspect-based sentiment analysis: Lord of the Flies William Golding, 2012-09-20 A plane crashes on a desert island and the only survivors, a group of schoolboys, assemble on the beach and wait to be rescued. By day they inhabit a land of bright fantastic birds and dark blue seas, but at night their dreams are haunted by the image of a terrifying beast. As the boys' delicate sense of order fades, so their childish dreams are transformed into something more primitive, and their behaviour starts to take on a murderous, savage significance. First published in 1954, Lord of the Flies is one of the most celebrated and widely read of modern classics. Now fully revised and updated, this educational edition includes chapter summaries, comprehension questions, discussion points, classroom activities, a biographical profile of Golding, historical context relevant to the novel and an essay on Lord of the Flies by William Golding entitled 'Fable'. Aimed at Key Stage 3 and 4 students, it also includes a section on literary theory for advanced or A-level students. The educational edition encourages original and independent thinking while guiding the student through the text - ideal for use in the classroom and at home. |
| aspect-based sentiment analysis: Neural Information Processing Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King, 2020-11-19 The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.* For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 papers included in this volume set were organized in topical sections as follows: data mining; healthcare analytics-improving healthcare outcomes using big data analytics; human activity recognition; image processing and computer vision; natural language processing; recommender systems; the 13th international workshop on artificial intelligence and cybersecurity; computational intelligence; machine learning; neural network models; robotics and control; and time series analysis. * The conference was held virtually due to the COVID-19 pandemic. |
| aspect-based sentiment analysis: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media Brij Gupta, Ahmed A. Abd El-Latif, Dragan Perakovic, 2021 This book explores the key concepts of data mining and utilizing them on online social media platforms, offering valuable insight into data mining approaches for big data and sentiment analysis in online social media and covering many important security and other aspects and current trends-- |
| aspect-based sentiment analysis: Oxford English Dictionary John A. Simpson, 2002-04-18 The Oxford English Dictionary is the internationally recognized authority on the evolution of the English language from 1150 to the present day. The Dictionary defines over 500,000 words, making it an unsurpassed guide to the meaning, pronunciation, and history of the English language. This new upgrade version of The Oxford English Dictionary Second Edition on CD-ROM offers unparalleled access to the world's most important reference work for the English language. The text of this version has been augmented with the inclusion of the Oxford English Dictionary Additions Series (Volumes 1-3), published in 1993 and 1997, the Bibliography to the Second Edition, and other ancillary material. System requirements: PC with minimum 200 MHz Pentium-class processor; 32 MB RAM (64 MB recommended); 16-speed CD-ROM drive (32-speed recommended); Windows 95, 98, Me, NT, 200, or XP (Local administrator rights are required to install and open the OED for the first time on a PC running Windows NT 4 and to install and run the OED on Windows 2000 and XP); 1.1 GB hard disk space to run the OED from the CD-ROM and 1.7 GB to install the CD-ROM to the hard disk: SVGA monitor: 800 x 600 pixels: 16-bit (64k, high color) setting recommended. Please note: for the upgrade, installation requires the use of the OED CD-ROM v2.0. |
| aspect-based sentiment analysis: 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI) IEEE Staff, 2021-10-28 This conference s topics are Computer Science, Artificial intelligence and all topics related with recent technology in Computer and Society 5 0 |
| aspect-based sentiment analysis: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) IEEE Staff, 2019-11-04 ICTAI 2019 The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading Conference of AI in the Computer Society providing a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies The conference facilitates the cross fertilization of AI ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications The ICTAI encompasses all technical aspects of specifying, developing and evaluating the theoretical underpinnings and applied mechanisms of the AI based components of computer tools (i e algorithms, architectures or languages) |
| aspect-based sentiment analysis: 2020 IEEE 18th International Conference on Industrial Informatics (INDIN) IEEE Staff, 2020-07-20 INDIN focuses on recent developments, deployments, technology trends, and research results in Industrial Informatics related fields from both industry and academia |
| aspect-based sentiment analysis: Predictive Clustering Hendrik Blockeel, Saso Dzeroski, Jan Struyf, Bernard Zenko, 2012-05-31 This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics. |
| aspect-based sentiment analysis: Sentiment Analysis Bing Liu, 2020-10-15 A comprehensive introduction to computational analysis of sentiments, opinions, emotions, and moods. Now including deep learning methods. |
| aspect-based sentiment analysis: Opinions, Sentiment, and Emotion in Text Bing Liu, 2015-06-04 This book gives a comprehensive introduction to all the core areas and many emerging themes of sentiment analysis. |
| aspect-based sentiment analysis: Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media Keikhosrokiani, Pantea, Pourya Asl, Moussa, 2022-02-18 Opinion mining and text analytics are used widely across numerous disciplines and fields in today’s society to provide insight into people’s thoughts, feelings, and stances. This data is incredibly valuable and can be utilized for a range of purposes. As such, an in-depth look into how opinion mining and text analytics correlate with social media and literature is necessary to better understand audiences. The Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media introduces the use of artificial intelligence and big data analytics applied to opinion mining and text analytics on literary works and social media. It also focuses on theories, methods, and approaches in which data analysis techniques can be used to analyze data to provide a meaningful pattern. Covering a wide range of topics such as sentiment analysis and stance detection, this publication is ideal for lecturers, researchers, academicians, practitioners, and students. |
| aspect-based sentiment analysis: ECAI 2023 K. Gal, A. Nowé, G.J. Nalepa, 2023-10-18 Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field. |
| aspect-based sentiment analysis: Data Engineering and Applications Jitendra Agrawal, |
| aspect-based sentiment analysis: Prominent Feature Extraction for Sentiment Analysis Basant Agarwal, Namita Mittal, 2015-12-14 The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis. |
英語「aspect」の意味・使い方・読み方 | Weblio英和辞書
「aspect」の意味・翻訳・日本語 - (もの・ことの)面、様相、(問題を見る)見地、角度、(家・部屋などの)向き、方位、(人の)顔つき、容貌(ようぼう)、(人事に影響を与えるという)星の相 …
「aspect」に関連した英語シソーラスの一覧 - Weblio英語類語検索
「はっきりとした特徴あるいは問題の要素」の意味で使われる「aspect, facet」の例文 he studied every facet of the question 彼は問題のあらゆる面を調査した
「ASPECT」に関連した英語例文の一覧と使い方 - Weblio
aspect or part of specified matter (one side, aspect or part) 例文帳に追加. さまざまな性質のうちのある一面 - EDR日英対訳辞書
An aspectの意味・使い方・読み方 | Weblio英和辞書
An aspectの意味や使い方 一面 - 約489万語ある英和辞典・和英辞典。発音・イディオムも分かる英語辞書。
英語類語 - Weblio辞書
aspect, view, vista, prospect, panorama, scene. 状況や話題などを評価する方法. a way of regarding situations or topics etc. view, position, perspective
ASPECTSの意味・使い方・読み方 | Weblio英和辞書
「ASPECTS」の意味・翻訳・日本語 - aspectの複数形。(もの・ことの)面、 様相｜Weblio英和・和英辞書
perfective aspectの意味・使い方・読み方 | Weblio英和辞書
perfective aspectの意味や使い方 【名詞】1動詞の様相で、完了した動作を表す(the aspect of a verb that expresses a completed action) - 約489万語ある英和辞典・和英辞典。発音・イディ …
every aspectの意味・使い方・読み方 | Weblio英和辞書
Every aspect of life: 例文帳に追加. 生活のあらゆる側面を支配します - 映画・海外ドラマ英語字幕翻訳辞書
英語「overall」の意味・読み方・表現 | Weblio英和辞書
「overall」の意味・翻訳・日本語 - (端から端まで)全部の、総体的な｜Weblio英和・和英辞書
英語「dimension」の意味・使い方・読み方 | Weblio英和辞書
aspect, dimensional, lateral, magnitude, point of view, profile, respect, side, sidedness, size, standpoint, view, viewpoint
英語「aspect」の意味・使い方・読み方 | Weblio英和辞書
「aspect」の意味・翻訳・日本語 - (もの・ことの)面、様相、(問題を見る)見地、角度、(家・部屋などの)向き、方位、(人の)顔つき、容貌(ようぼう)、(人事に影響を与えるという)星の相、 …
「aspect」に関連した英語シソーラスの一覧 - Weblio英語類語検索
「はっきりとした特徴あるいは問題の要素」の意味で使われる「aspect, facet」の例文 he studied every facet of the question 彼は問題のあらゆる面を調査した
「ASPECT」に関連した英語例文の一覧と使い方 - Weblio
aspect or part of specified matter (one side, aspect or part) 例文帳に追加. さまざまな性質のうちのある一面 - EDR日英対訳辞書
An aspectの意味・使い方・読み方 | Weblio英和辞書
An aspectの意味や使い方 一面 - 約489万語ある英和辞典・和英辞典。発音・イディオムも分かる英語辞書。
英語類語 - Weblio辞書
aspect, view, vista, prospect, panorama, scene. 状況や話題などを評価する方法. a way of regarding situations or topics etc. view, position, perspective
ASPECTSの意味・使い方・読み方 | Weblio英和辞書
「ASPECTS」の意味・翻訳・日本語 - aspectの複数形。(もの・ことの)面、 様相｜Weblio英和・和英辞書
perfective aspectの意味・使い方・読み方 | Weblio英和辞書
perfective aspectの意味や使い方 【名詞】1動詞の様相で、完了した動作を表す(the aspect of a verb that expresses a completed action) - 約489万語ある英和辞典・和英辞典。発音・イディ …
every aspectの意味・使い方・読み方 | Weblio英和辞書
Every aspect of life: 例文帳に追加. 生活のあらゆる側面を支配します - 映画・海外ドラマ英語字幕翻訳辞書
英語「overall」の意味・読み方・表現 | Weblio英和辞書
「overall」の意味・翻訳・日本語 - (端から端まで)全部の、総体的な｜Weblio英和・和英辞書
英語「dimension」の意味・使い方・読み方 | Weblio英和辞書
aspect, dimensional, lateral, magnitude, point of view, profile, respect, side, sidedness, size, standpoint, view, viewpoint
