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| applications of 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-- |
| applications of sentiment analysis: Sentiment Analysis and Knowledge Discovery in Contemporary Business Rajput, Dharmendra Singh, Thakur, Ramjeevan Singh, Basha, S. Muzamil, 2018-08-31 In the era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through online collaborative media. However, conducting sentiment analysis on these platforms can be challenging, especially for business professionals who are using them to collect vital data. Sentiment Analysis and Knowledge Discovery in Contemporary Business is an essential reference source that discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business environments. Featuring research on topics such as knowledge retrieval and knowledge updating, this book is ideally designed for business managers, academicians, business professionals, researchers, graduate-level students, and technology developers seeking current research on data collection and management to drive profit. |
| applications of 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. |
| applications of sentiment analysis: Opinion Mining and Sentiment Analysis Bo Pang, Lillian Lee, 2008 This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. |
| applications of sentiment analysis: Exploring the Power of Electronic Word-Of-Mouth in the Services Industry Hans Ruediger Kaufmann, Sandra Maria Correia Loureiro, 2019-08 This book examines the importance and the effective utilization of eWOM content for the positioning of products and services that illustrate the value of user generated content for influencing customer decision making in diverse business sectors-- |
| applications of 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 |
| applications of 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 |
| applications of sentiment analysis: Practical Text Analytics Murugan Anandarajan, Chelsey Hill, Thomas Nolan, 2018-10-19 This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information. |
| applications of sentiment analysis: Data Modelling Techniques and Various Applications of Sentiment Analysis Sergiu Limboi, 2024 |
| applications of sentiment analysis: New Opportunities for Sentiment Analysis and Information Processing Sharaff, Aakanksha, Sinha, G. R., Bhatia, Surbhi, 2021-06-25 Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students. |
| applications of sentiment analysis: Sentiment Analysis and Ontology Engineering Witold Pedrycz, Shyi-Ming Chen, 2016-03-22 This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology –oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students. |
| applications of 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. |
| applications of sentiment analysis: Sentiment Analysis and its Application in Educational Data Mining Soni Sweta, |
| applications of sentiment analysis: Emerging Technologies for Healthcare Monika Mangla, Nonita Sharma, Poonam Garg, Vaishali Wadhwa, Thirunavukkarasu K, Shahnawaz Khan, 2021-08-17 “Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques. The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions. This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms. |
| applications of 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. |
| applications of sentiment analysis: Sentiment Analysis for Social Media Carlos A. Iglesias, Antonio Moreno, 2020-04-02 Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection. |
| applications of 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. |
| applications of 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 |
| applications of sentiment analysis: Responsible AI and Analytics for an Ethical and Inclusive Digitized Society Denis Dennehy, Anastasia Griva, Nancy Pouloudi, Yogesh K. Dwivedi, Ilias Pappas, Matti Mäntymäki, 2021-08-25 This volume constitutes the proceedings of the 20th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2021, held in Galway, Ireland, in September 2021.* The total of 57 full and 8 short papers presented in these volumes were carefully reviewed and selected from 141 submissions. The papers are organized in the following topical sections: AI for Digital Transformation and Public Good; AI & Analytics Decision Making; AI Philosophy, Ethics & Governance; Privacy & Transparency in a Digitized Society; Digital Enabled Sustainable Organizations and Societies; Digital Technologies and Organizational Capabilities; Digitized Supply Chains; Customer Behavior and E-business; Blockchain; Information Systems Development; Social Media & Analytics; and Teaching & Learning. *The conference was held virtually due to the COVID-19 pandemic. |
| applications of sentiment analysis: Computing Attitude and Affect in Text: Theory and Applications James G. Shanahan, Yan Qu, Janyce Wiebe, 2006-01-17 Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the “factual” aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion. Various conceptual models and computational methods are presented. The models explored in this book include the following: distinguishing attitudes from simple factual assertions; distinguishing between the author’s reports from reports of other people’s opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, including indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups about consumer products, political issues, etc. ; brand and reputation management; discovering and predicting consumer and voting trends; analyzing client discourse in therapy and counseling; determining relations between scientific texts by finding reasons for citations; generating more appropriate texts and making agents more believable; and creating writers’ aids. The studies reported here are carried out on different languages such as English, French, Japanese, and Portuguese. Difficult challenges remain, however. It can be argued that analyzing attitude and affect in text is an “NLP”-complete problem. |
| applications of sentiment analysis: Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications Gary Miner, 2012-01-11 The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities-- |
| applications of sentiment analysis: Emerging Concepts in Technology-Enhanced Language Teaching and Learning Zou, Bin, Thomas, Michael, Barr, David, Jia, Wen, 2022-01-21 For years, language teachers have increasingly been using technologies of all kinds, from computers to smartphones, to help their students learn. Current trends in TELTL (technology-enhanced language teaching and learning), such as artificial intelligence, virtual reality, augmented reality, gamification, and social networking, appear to represent major shifts in the digital language learning landscape. However, various applications of technology to mediate language learning may be informed by reflecting not only on the present but perhaps more importantly on relevant insights from past research and practice. Emerging Concepts in Technology-Enhanced Language Teaching and Learning explores the recent development of the new technologies for language teaching and learning to gain insights into and synergy of the theories, pedagogies, technological design, and evaluation of TELTL environments for comprehending the trends and strategies of the new digital era as well as investigate the possibility of future TELTL research direction. The book includes trends shaped by contemporary issues such as the COVID-19 pandemic. Covering topics such as digital education tools, L2 learnings, and sentiment analysis, this book serves as an essential resource for researchers, language teachers, educational software developers, administrators, IT consultants, technologists, professors, pre-service teachers, academicians, and students. |
| applications of sentiment analysis: Applied Data Science in Tourism Roman Egger, 2022-01-31 Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a how-to approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau |
| applications of sentiment analysis: Learn Emotion Analysis with R Partha Majumdar, 2021-06-02 Learn to assess textual data and extract sentiments using various text analysis R packages KEY FEATURES ● In-depth coverage on core principles, challenges, and application of Emotion Analysis. ● Includes real-world examples to simplify practical uses of R, Shiny, and various popular NLP techniques. ● Covers different strategies used in Sentiment and Emotion Analysis. DESCRIPTION This book covers how to conduct Emotion Analysis based on Lexicons. Through a detailed code walkthrough, the book will explain how to develop systems for Sentiment and Emotion Analysis from popular sources of data, including WhatsApp, Twitter, etc. The book starts with a discussion on R programming and Shiny programming as these will lay the foundation for the system to be developed for Emotion Analysis. Then, the book discusses essentials of Sentiment Analysis and Emotion Analysis. The book then proceeds to build Shiny applications for Emotion Analysis. The book rounds off with creating a tool for Emotion Analysis from the data obtained from Twitter and WhatsApp. Emotion Analysis can be also performed using Machine Learning. However, this requires labeled data. This is a logical next step after reading this book. WHAT YOU WILL LEARN ● Learn the essentials of Sentiment Analysis. ● Learn the essentials of Emotion Analysis. ● Conducting Emotion Analysis using Lexicons. ● Learn to develop Shiny applications. ● Understanding the essentials of R programming for developing systems for Emotion Analysis. WHO THIS BOOK IS FOR This book aspires to teach NLP users, ML engineers, and AI engineers who want to develop a strong understanding of Emotion and Sentiment Analysis. No prior knowledge of R programming is needed. All you need is just an open mind to learn and explore this concept. TABLE OF CONTENTS Section 1 Introduction to R Programming 1 Getting Started with R 2 Simple Operations using R 3 Developing Simple Applications in R Section 2 Introduction to Shiny Programming 4 Structure of Shiny Applications 5 Shiny Application 1 6 Shiny Application 2 Section 3 Emotion Analysis 7 Sentiment Analysis 8 Emotion Analysis 9 ZEUSg Section 4 Twitter Data Analysis 10 Introduction to Twitter Data Analysis 11 Emotion Analysis on Twitter Data 12 Chidiya BONUS CHAPTER WhatsApp Chat Analysis |
| applications of 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. |
| applications of 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. |
| applications of sentiment analysis: Advances in Social Media Analysis Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker, 2015-06-05 This volume presents a collection of carefully selected contributions in the area of social media analysis. Each chapter opens up a number of research directions that have the potential to be taken on further in this rapidly growing area of research. The chapters are diverse enough to serve a number of directions of research with Sentiment Analysis as the dominant topic in the book. The authors have provided a broad range of research achievements from multimodal sentiment identification to emotion detection in a Chinese microblogging website. The book will be useful to research students, academics and practitioners in the area of social media analysis. |
| applications of sentiment analysis: Examining the Impact of Deep Learning and IoT on Multi-Industry Applications Raut, Roshani, Mihovska, Albena Dimitrova, 2021-01-29 Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings. Examining the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers’ points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering. |
| applications of 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 |
| applications of sentiment analysis: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
| applications of sentiment analysis: Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics Kumar, Anil, Dash, Manoj Kumar, Trivedi, Shrawan Kumar, Panda, Tapan Kumar, 2016-10-25 The success of any organization is largely dependent on positive feedback and repeat business from patrons. By utilizing acquired marketing data, business professionals can more accurately assess practices, services, and products that their customers find appealing. The Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics features innovative research and implementation practices of analytics in marketing research. Highlighting various techniques in acquiring and deciphering marketing data, this publication is a pivotal reference for professionals, managers, market researchers, and practitioners interested in the observation and utilization of data on marketing trends to promote positive business practices. |
| applications of sentiment analysis: Computational Intelligence Applications for Text and Sentiment Data Analysis Dipankar Das, Anup Kumar Kolya, Abhishek Basu, Soham Sarkar, 2023-07-14 Approx.330 pagesApprox.330 pages |
| applications of sentiment analysis: Handbook of Research on Emerging Trends and Applications of Machine Learning Solanki, Arun, Kumar, Sandeep, Nayyar, Anand, 2019-12-13 As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning. |
| applications of sentiment analysis: Data Analysis with Python David Taieb, 2018-12-31 Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key FeaturesBridge your data analysis with the power of programming, complex algorithms, and AIUse Python and its extensive libraries to power your way to new levels of data insightWork with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time seriesExplore this modern approach across with key industry case studies and hands-on projectsBook Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learnA new toolset that has been carefully crafted to meet for your data analysis challengesFull and detailed case studies of the toolset across several of today’s key industry contextsBecome super productive with a new toolset across Python and Jupyter NotebookLook into the future of data science and which directions to develop your skills nextWho this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development. |
| applications of 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. |
| applications of sentiment analysis: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| applications of sentiment analysis: The SAGE Handbook of Social Media Research Methods Luke Sloan, Anabel Quan-Haase, 2017-01-26 With coverage of the entire research process in social media, data collection and analysis on specific platforms, and innovative developments in the field, this handbook is the ultimate resource for those looking to tackle the challenges that come with doing research in this sphere. |
| applications of sentiment analysis: Advanced Deep Learning Applications in Big Data Analytics Bouarara, Hadj Ahmed, 2020-10-16 Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students. |
| applications of sentiment analysis: Principal Component Analysis I.T. Jolliffe, 2013-03-09 Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters. |
| applications of sentiment analysis: HCI in Business, Government, and Organizations Fiona Fui-Hoon Nah, Bo Sophia Xiao, 2018-07-09 This book constitutes the refereed proceedings of the 5th International Conference on HCI in Business, Government and Organizations, HCIBGO 2018, held as part of the 20th International Conference on Human-Computer Interaction, HCII 2018, in Las Vegas, NV, USA. The 1171 full papers and 160 posters presented at the 14 co-located HCII 2018 conferences were carefully reviewed and selected from a total of 4346 submissions. The papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers included in this volume cover the following topics: information systems in business; electronic commerce and consumer behavior; social media and social communities in business; social innovation; and business analytics and visualization. |
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A Comprehensive Study on Methods, Challenges and …
A Comprehensive Study on Methods, Challengesand Applications of Sentiment Analysis: A Survey International organization of Scientific Research 15 | Page V. LITERATURE REVIEW …
A Review On Sentiment Analysis Methodologies, Practices …
international journal of scientific & technology research volume 9, issue 02, february 2020 issn 2277-8616 ijstr©2020 www.ijstr.org analysis
Social Media Sentiment Analysis for Brand Monitoring
Figure.2. Process of Sentiment Analysis . IV.APPLICATIONS Social media sentiment analysis offers a wide range of applications for brand monitoring, helping businesses stay attuned to …
Understanding Sentiment Analysis with VADER: A
Jun 23, 2024 · Sentiment analysis, also known as opinion mining, is a pivotal technique in natural language processing (NLP) that involves identifying and extracting subjective information from …
Survey on sentiment analysis: evolution of research methods …
tions, and unsolved problems in the eld of sentiment analysis (Ravi and Ravi 2015). Existing surveys of the applications of sentiment analysis have focused more on the domains of market …
Evolving techniques in sentiment analysis: a comprehensive …
process of sentiment analysis, including data collection, pre-processing, and feature extraction. ‘Sentiment Analysis Approaches’ provides an overview of the different techniques and …
Text Mining and Sentiment Analysis - irjet.net
the given review and the analysis process in corporates natural language processing (NLP), computational linguistics, text analytics and classifying the polarity of the opinion. In the field …
Segmentation of Tweets with URLs and its Applications to …
May 18, 2021 · timent analysis in the sections “Sentiment Analysis” and “Case Study: Sentiment Analysis”. Motivation In this section, we give quantitative evidence about the need to treat …
Natural Language Processing, Sentiment Analysis and Clinical
Furthermore, we look at some applications of sentiment analysis and application of NLP to mental health. The reader will also learn about the NLTK toolkit that implements various NLP theories …
An Overview of Sentiment Analysis in Social Media and its …
Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Re-cent years, on the other hand, have …
Using sentiment analysis to measure customer satisfaction: …
applications, sentiment analysis faces several challenges. Sarcasm, idiomatic expressions, and context-dependent sentiments are particularly difficult for models to accurately interpret. …
Discourse Analysis and Its Applications - ACL Anthology
plications of discourse analysis such as machine translation and its evaluation, sentiment analysis, and abstractive summarization. In the final part of the tutorial, we cover conver …
e-ISSN: 2583-3472 Sentiment Analysis Web App Using NLP
Sentiment analysis is a crucial tool for understanding people's emotions, opinions, and attitudes towards products, services, and events, particularly in the business world. This paper presents …
Sentiment Analysis and Its Applications in Recommender …
Sentiment Analysis and Its Applications in Recommender Systems Bui Thanh Hung, Prasun Chakrabarti, and Prasenjit Chatterjee Abstract E-commerce and social network make …
An Analysis of Sentiment: Methods, Applications, and …
Eng. Proc. 2023, 59, 68 2 of 11 The definition of the sentiment analysis procedure in detail, as well as the identification of well-known techniques for performing it in this paper; it was ...
Review of NLP Applications in the Field of Text Sentiment …
emotion analysis framework, so the emotional dictionary of automatic construction technology constantly attention and development. 4 Domain-Adaptive Emotion Analysis 4.1 Cross-Domain …
A survey on sentiment analysis and its applications - Springer
Keywords Sentiment analysis Feature selection Deep learning Machine learning Optimization 1 Introduction Social media platforms such as Twitter, Facebook, and ... applications, tools, …
LEVERAGING TEXTBLOB AND TWITTER API FOR …
APPLICATIONS Sentiment analysis serves purposes, across industries. Here are some key applications; 1. Managing Brand Reputation. Monitoring sentiments surrounding a brand and …
Sentiment Analysis and Emotion Detection Using …
sentiment analysis applications, this presents issues like data scarcity, domain adaptation, and computational complexity. This study focuses on multilingual sentiment analysis and emotion …
Enhancing Sentiment Analysis on Social Media Data with …
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 15, No. 5, 2024 970 | P a g e www.ijacsa.thesai.org Enhancing Sentiment Analysis on Social Media Data …
The Applications of Sentiment Analysis for Russian …
applications of sentiment analysis of the Russian language content since it was not reviewed before. Secondly, we cat-egorised existing studies based on the utilised data source,
The Process of Sentiment Analysis: A Study - ijcaonline.org
applications of sentiment analysis along with the workflow that describes the execution of this analysis. The recent techniques used in the analysis have been described briefly and the …
Sentiment Analysis Approaches and Applications: A Survey
of sentiment analysis. Therefore, sentiment investigation idea is proposed. Among various applications of Natural Language processing (NLP) and Machine Learning (ML) Sentiment …
Comprehensive Review of Sentiment Analysis: Techniques, …
APPLICATIONS OF SENTIMENT ANALYSIS Sentiment Analysis has a wide range of applications across various domains. 1. Customer Feedback: Businesses leverage sentiment …
Sentiment Analysis Main Tasks and Applications - Korea …
the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art algorithms, methodologies and techniques have been categorized and summarized to …
Multimodal sentiment analysis: A systematic review of …
Jun 13, 2022 · NLP due to its multiple applications in sentiment analysis, review-based systems, healthcare, and other fields [3]. The idea of detecting emotion in news headlines has been …
Sentiment Analysis: A Perspective on its Past, Present and …
used for solving the sentiment analysis tasks. Section 7 presents the various applications of sentiment analysis. Lastly, section 8 discusses the various issues that turn out as open …
AI-Driven Sentiment Analytics: Unlocking Business Value in …
C. E-Commerce Applications Sentiment analysis has found numerous applications in e-commerce, particularly in customer feedback analysis and product review mining. [10] …
Text Mining: Overview, Applications and Issues - Stony …
• Sentiment analysis - analysis of data for predicting desired results • Software applications – used by major firms to automate analysis • Academic, online media, digital humanities, etc. “Getting …
Privacy-Preserving Techniques in Sentiment Analysis
Sentiment analysis plays a crucial role in multiple industries and applications: • Business and Marketing: Companies use sentiment analysis to assess customer opinions, improve products, …
SentimentGPT: Exploiting GPT for Advanced Sentiment …
Real-life applications of sentiment analysis have surged, with organizations employing it for a variety of purposes, such as monitoring brand perception [24], analyzing political campaigns …
APPLICATIONS OF SENTIMENT ANALYSIS
• Sentiment Match + Aspect Coverage (SMAC) • Pick a summary with good sentiment match and has good diversity over the aspects. • It is possible to have good sentiment match and still pick …
Multimodal Sentiment Analysis: A Survey - arXiv.org
sentiment analysis. In the past, sentiment analysis has mostly focused on a single modality (visual modality, speech modal-ity, or text modality) [12]. Text-based sentiment anal-ysis [13–15] has …
Advancements In Sentiment Analysis: Techniques, …
base to guide future sentiment analysis research and applications. II. HISTORICAL CONTEXT The history of sentiment analysis goes back to early work in computational linguistics, where …
Financial Sentiment Analysis: Techniques and Applications
Financial Sentiment Analysis: Techniques and Applications 220:5 Fig. 1. Financial sentiment, investor sentiment, and market sentiment. reports ...
Sentiment Analysis - Methods, Applications & Challenges
on Sentiment Analysis, methods, applications and challenges. This review discusses methods for feature selection & sentiment classification in detail which will prove helpful for user in …
Sentiment analysis using deep learning techniques: a
Sentiment analysis, also referred to as opinion mining, is a valuable technique for extracting sentiments and subjective information from textual data [1, 2]. With the exponential growth of …
Techniques and Applications for Sentiment Analysis - GitHub …
Techniques and Applications for Sentiment Analysis Airton Bordin Junior airtonbjunior@gmail.com Prof. Dr. Nádia Félix Felipe da Silva nadia@inf.ufg.br Ronen Feldman ... areas where …
Sentiment Analysis - University of Illinois Chicago
1.1 Sentiment Analysis Applications 4 1.2 Sentiment Analysis Research 8 1.2.1 Different Levels of Analysis 9 1.2.2 Sentiment Lexicon and Its Issues 10 1.2.3 Analyzing Debates and Comments …
Comprehensive Review of Sentiment Analysis: Techniques, …
sentiment analysis, highlighting their applications across diverse sectors such as customer feedback analysis, social media monitoring, and healthcare. ... NLP algorithms, emotion …
ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment …
applications. Sentiment analysis, also known as opinion mining [1], sentiment classification [2], or emotion AI [3], is a field at the intersection of Natural Language Processing (NLP), Machine …
blackSAHSD: Enhancing Hate Speech Detection in LLM …
Applications via Sentiment Analysis and Few-Shot Learning Abstract As large language models (LLMs) increasingly power web appli-cations, including social networks, the challenge of …
Sentiment analysis algorithms and applications: A survey
an entity while Sentiment Analysis identifies the sentiment expressed in a text then analyzes it. Therefore, the target of SA is to find opinions, identify the sentiments they express, and then …
A Review On Sentiment Analysis Methodologies, Practices …
A Review On Sentiment Analysis Methodologies, Practices And Applications Pooja Mehta, Dr.Sharnil Pandya Abstract: The Sentiment Analysis is sometimes a technique to look at the …
The Applications of Sentiment Analysis for Russian …
various dimensions. Firstly, targeted the applications of sentiment analysis rather than existing sentiment analysis approachesandtheirclassi˝cationquality.Wefocusedonthe applications of …
Potential Applications of Sentiment Analysis in Educational …
Sentiment analysis applications have been widely used in fields outside of education. Researchers have firmly demonstrated the utility of sentiment analyses by successfully …
Fundamentals of Sentiment Analysis and Its Applications
Fundamentals of Sentiment Analysis and Its Applications Mohsen Farhadloo and Erik Rolland Abstract The problem of identifying people’s opinions expressed in written language is a …
Multimodal sentiment analysis: A systematic review of …
NLP due to its multiple applications in sentiment analysis, review-based systems, healthcare, and other fields [3]. The idea of detecting emotion in news headlines has been discussed by a …
