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| benefits 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 |
| benefits 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. |
| benefits 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. |
| benefits 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 |
| benefits 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. |
| benefits 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. |
| benefits of sentiment analysis: Social Big Data Analytics Bilal Abu-Salih, Pornpit Wongthongtham, Dengya Zhu, Kit Yan Chan, Amit Rudra, 2021-03-10 This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display. |
| benefits of sentiment analysis: Sentiment Analysis and its Application in Educational Data Mining Soni Sweta, |
| benefits 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. |
| benefits 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. |
| benefits 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. |
| benefits of sentiment analysis: ANALYSE DU SENTIMENT DU MARCHÉ AVEC LE BIG DATA Marcel Souza, Analyse du Sentiment du Marché avec le Big Data est un guide essentiel pour les traders, les analystes et les investisseurs qui souhaitent exploiter le pouvoir des données massives pour comprendre et anticiper les mouvements du marché. Grâce à des techniques avancées de traitement et d'analyse des données, ce livre vous permettra de mieux comprendre les émotions et les comportements des acteurs du marché, souvent invisibles dans les données traditionnelles. Découvrez comment le Big Data peut révéler des tendances cachées et offrir des perspectives uniques sur la dynamique des marchés financiers. Ce livre explore en profondeur les différentes méthodes d'analyse du sentiment, y compris l'exploration de données des réseaux sociaux, des forums financiers et d'autres sources en ligne. Il explique comment utiliser ces données pour identifier les signaux d'achat et de vente, évaluer l'impact des événements économiques et politiques sur les marchés et mieux comprendre la psychologie des investisseurs. Que vous soyez un novice ou un expert, ce livre vous fournira les outils nécessaires pour intégrer ces techniques dans votre stratégie de trading. En combinant les concepts traditionnels de l'analyse technique et fondamentale avec des approches basées sur le Big Data, Analyse du Sentiment du Marché avec le Big Data offre une perspective nouvelle et puissante sur la manière de réussir dans le monde complexe des investissements. Vous apprendrez à interpréter les fluctuations des prix, à identifier les opportunités et à prendre des décisions plus informées grâce à l'analyse de millions de points de données en temps réel. Avec des études de cas concrètes et des exemples pratiques, cet ouvrage est une ressource indispensable pour quiconque souhaite maîtriser l'art de l'analyse du sentiment de marché à l'ère du numérique. Plongez dans l'univers du Big Data et transformez votre approche de l'investissement pour maximiser vos rendements tout en minimisant les risques. |
| benefits 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. |
| benefits of sentiment analysis: MarketPsych Richard L. Peterson, Frank F. Murtha, 2010-07-30 An investor's guide to understanding the most elusive (yet most important) aspect of successful investing - yourself. Why is it that the investing performance of so many smart people reliably and predictably falls short? The answer is not that they know too little about the markets. In fact, they know too little about themselves. Combining the latest findings from the academic fields of behavioral finance and experimental psychology with the down-and-dirty real-world wisdom of successful investors, Drs. Richard Peterson and Frank Murtha guide both new and experienced investors through the psychological learning process necessary to achieve their financial goals. In an easy and entertaining style that masks the book’s scientific rigor, the authors make complex scientific insights readily understandable and actionable, shattering a number of investing myths along the way. You will gain understanding of your true investing motivations, learn to avoid the unseen forces that subvert your performance, and build your investor identity - the foundation for long-lasting investing success. Replete with humorous games, insightful self-assessments, entertaining exercises, and concrete planning tools, this book goes beyond mere education. MarketPsych: How to Manage Fear and Build Your Investor Identity functions as a psychological outfitter for your unique investing journey, providing the tools, training and equipment to help you navigate the right paths, stay on them, and see your journey through to success. |
| benefits 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. |
| benefits of 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 |
| benefits of sentiment analysis: Sentiment Analysis in the Medical Domain Kerstin Denecke, 2023-05-24 Sentiment analysis deals with extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. Medical sentiment analysis refers to the identification and analysis of sentiments or emotions expressed in free-textual documents with a scope on healthcare and medicine. This fascinating problem offers numerous application areas in the domain of medicine, but also research challenges. The book provides a comprehensive introduction to the topic. The primary purpose is to provide the necessary background on medical sentiment analysis, ranging from a description of the notions of medical sentiment to use cases that have been considered already and application areas of relevance. Medical sentiment analysis uses natural language processing (NLP), text analysis and machine learning to realise the process of extracting and classifying statements regarding expressed opinion and sentiment. The book offers a comprehensive overview on existing methods of sentiment analysis applied to healthcare resources or health-related documents. It concludes with open research avenues providing researchers indications which topics still have to be developed in more depth. |
| benefits of sentiment analysis: A First Course in Artificial Intelligence Osondu Oguike, 2021-07-14 The importance of Artificial Intelligence cannot be over-emphasised in current times, where automation is already an integral part of industrial and business processes. A First Course in Artificial Intelligence is a comprehensive textbook for beginners which covers all the fundamentals of Artificial Intelligence. Seven chapters (divided into thirty-three units) introduce the student to key concepts of the discipline in simple language, including expert system, natural language processing, machine learning, machine learning applications, sensory perceptions (computer vision, tactile perception) and robotics. Each chapter provides information in separate units about relevant history, applications, algorithm and programming with relevant case studies and examples. The simplified approach to the subject enables beginners in computer science who have a basic knowledge of Java programming to easily understand the contents. The text also introduces Python programming language basics, with demonstrations of natural language processing. It also introduces readers to the Waikato Environment for Knowledge Analysis (WEKA), as a tool for machine learning. The book is suitable for students and teachers involved in introductory courses in undergraduate and diploma level courses which have appropriate modules on artificial intelligence. |
| benefits of 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. |
| benefits of sentiment analysis: Text Analytics with SAS , 2019-06-14 SAS provides many different solutions to investigate and analyze text and operationalize decisioning. Several impressive papers have been written to demonstrate how to use these techniques. We have carefully selected a handful of these from recent Global Forum contributions to introduce you to the topic and let you sample what each has to offer. Also available free as a PDF from sas.com/books. |
| benefits of sentiment analysis: Computational Intelligence in Pattern Recognition Asit Kumar Das, Janmenjoy Nayak, Bighnaraj Naik, Soumi Dutta, Danilo Pelusi, 2020-02-19 This book features high-quality research papers presented at the 2nd International Conference on Computational Intelligence in Pattern Recognition (CIPR 2020), held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 4–5 January 2020. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments. |
| benefits of sentiment analysis: Why I’m No Longer Talking to White People About Race Reni Eddo-Lodge, 2020-11-12 'Every voice raised against racism chips away at its power. We can't afford to stay silent. This book is an attempt to speak' The book that sparked a national conversation. Exploring everything from eradicated black history to the inextricable link between class and race, Why I'm No Longer Talking to White People About Race is the essential handbook for anyone who wants to understand race relations in Britain today. THE NO.1 SUNDAY TIMES BESTSELLER WINNER OF THE BRITISH BOOK AWARDS NON-FICTION NARRATIVE BOOK OF THE YEAR 2018 FOYLES NON-FICTION BOOK OF THE YEAR BLACKWELL'S NON-FICTION BOOK OF THE YEAR WINNER OF THE JHALAK PRIZE LONGLISTED FOR THE BAILLIE GIFFORD PRIZE FOR NON-FICTION LONGLISTED FOR THE ORWELL PRIZE SHORTLISTED FOR A BOOKS ARE MY BAG READERS AWARD |
| benefits of sentiment analysis: Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Mallick, Khaled Shaalan, 2022-04-19 The book discusses how augmented intelligence can increase the efficiency and speed of diagnosis in healthcare organizations. The concept of augmented intelligence can reflect the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods. It includes real-life case studies highlighting impact of augmented intelligence in health care. The book offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in healthcare challenges. It presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It also presents specific applications of augmented intelligence in health care, and architectural models and frameworks-based augmented solutions. |
| benefits of sentiment analysis: Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines Management Association, Information Resources, 2022-06-10 The rise of internet and social media usage in the past couple of decades has presented a very useful tool for many different industries and fields to utilize. With much of the world’s population writing their opinions on various products and services in public online forums, industries can collect this data through various computational tools and methods. These tools and methods, however, are still being perfected in both collection and implementation. Sentiment analysis can be used for many different industries and for many different purposes, which could better business performance and even society. The Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines discusses the tools, methodologies, applications, and implementation of sentiment analysis across various disciplines and industries such as the pharmaceutical industry, government, and the tourism industry. It further presents emerging technologies and developments within the field of sentiment analysis and opinion mining. Covering topics such as electronic word of mouth (eWOM), public security, and user similarity, this major reference work is a comprehensive resource for computer scientists, IT professionals, AI scientists, business leaders and managers, marketers, advertising agencies, public administrators, government officials, university administrators, libraries, students and faculty of higher education, researchers, and academicians. |
| benefits of sentiment analysis: United States Code United States, 2001 |
| benefits of sentiment analysis: Artificial Intelligence for Business Rajendra Akerkar, 2018-08-11 This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes. |
| benefits of sentiment analysis: General Theory Of Employment , Interest And Money John Maynard Keynes, 2016-04 John Maynard Keynes is the great British economist of the twentieth century whose hugely influential work The General Theory of Employment, Interest and * is undoubtedly the century's most important book on economics--strongly influencing economic theory and practice, particularly with regard to the role of government in stimulating and regulating a nation's economic life. Keynes's work has undergone significant revaluation in recent years, and Keynesian views which have been widely defended for so long are now perceived as at odds with Keynes's own thinking. Recent scholarship and research has demonstrated considerable rivalry and controversy concerning the proper interpretation of Keynes's works, such that recourse to the original text is all the more important. Although considered by a few critics that the sentence structures of the book are quite incomprehensible and almost unbearable to read, the book is an essential reading for all those who desire a basic education in economics. The key to understanding Keynes is the notion that at particular times in the business cycle, an economy can become over-productive (or under-consumptive) and thus, a vicious spiral is begun that results in massive layoffs and cuts in production as businesses attempt to equilibrate aggregate supply and demand. Thus, full employment is only one of many or multiple macro equilibria. If an economy reaches an underemployment equilibrium, something is necessary to boost or stimulate demand to produce full employment. This something could be business investment but because of the logic and individualist nature of investment decisions, it is unlikely to rapidly restore full employment. Keynes logically seizes upon the public budget and government expenditures as the quickest way to restore full employment. Borrowing the * to finance the deficit from private households and businesses is a quick, direct way to restore full employment while at the same time, redirecting or siphoning |
| benefits of sentiment analysis: Social Media Data Extraction and Content Analysis Hai-Jew, Shalin, 2016-08-01 In today’s society, the utilization of social media platforms has become an abundant forum for individuals to post, share, tag, and, in some cases, overshare information about their daily lives. As significant amounts of data flood these venues, it has become necessary to find ways to collect and evaluate this information. Social Media Data Extraction and Content Analysis explores various social networking platforms and the technologies being utilized to gather and analyze information being posted to these venues. Highlighting emergent research, analytical techniques, and best practices in data extraction in global electronic culture, this publication is an essential reference source for researchers, academics, and professionals. |
| benefits of sentiment analysis: Data Science—Analytics and Applications Peter Haber, Thomas J. Lampoltshammer, Manfred Mayr, 2024-01-03 Based on the overall digitalization in all spheres of our lives, Data Science and Artificial Intelligence (AI) are nowadays cornerstones for innovation, problem solutions, and business transformation. Data, whether structured or unstructured, numerical, textual, or audiovisual, put in context with other data or analyzed and processed by smart algorithms, are the basis for intelligent concepts and practical solutions. These solutions address many application areas such as Industry 4.0, the Internet of Things (IoT), smart cities, smart energy generation, and distribution, and environmental management. Innovation dynamics and business opportunities for effective solutions for the essential societal, environmental, or health challenges, are enabled and driven by modern data science approaches. However, Data Science and Artificial Intelligence are forming a new field that needs attention and focused research. Effective data science is only achieved in a broad and diverse discourse – when data science experts cooperate tightly with application domain experts and scientists exchange views and methods with engineers and business experts. Thus, the 5th International Data Science Conference (iDSC 2023) brings together researchers, scientists, business experts, and practitioners to discuss new approaches, methods, and tools made possible by data science. |
| benefits of sentiment analysis: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| benefits of sentiment analysis: Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 1 Amit Kumar, |
| benefits of sentiment analysis: Artificial Intelligence Dr. V. Deepa, Dr. Jeyanthi, Mrs. P.R.Sukanya Sridevi, Augustin Kirubakaran, 2024-09-27 Artificial Intelligence delves into the transformative world of AI, exploring its foundational theories, practical applications, and ethical implications. Covering core topics like machine learning, neural networks, and natural language processing, the book offers a comprehensive view of AI's potential to reshape industries, enhance decision-making, and drive innovation. With discussions on challenges, advancements, and future trends, this resource serves as an essential guide for students, professionals, and enthusiasts eager to understand and engage with the dynamic field of artificial intelligence. |
| benefits of sentiment analysis: Linguistic Inquiry and Word Count James W. Pennebaker, M. E. Francis, 1999-04-01 Language, whether spoken or written, is an important window into people's emotional and cognitive worlds. Text analysis of these narratives, focusing on specific words or classes of words, has been used in numerous research studies including studies of emotional, cognitive, structural, and process components of individuals' verbal and written language. It was in this research context that the LIWC program was developed. The program analyzes text files on a word-by-word basis, calculating percentage words that match each of several language dimensions. Its output is a text file that can be opened in any of a variety of applications, including word processors and spreadsheet programs. The program has 68 pre-set dimensions (output variables) including linguistic dimensions, word categories tapping psychological constructs, and personal concern categories, and can accommodate user-defined dimensions as well. Easy to install and use, this software offers researchers in social, personality, clinical, and applied psychology a valuable tool for quantifying the rich but often slippery data provided in the form of personal narratives. The software comes complete on one 31/2 diskette and runs on any Windows-based computer. |
| benefits of sentiment analysis: Advanced Concepts, Methods, and Applications in Semantic Computing Olawande Daramola, Thomas Moser, 2020-11 The book provides a sound theoretical foundation for the application of semantic methods, concepts, technologies for practical problem solving offering original research on advanced concepts, methods, algorithms, technologies, and applications of semantic computing in real-world situations-- |
| benefits of sentiment analysis: Handbook on Tourism and Social Media Gursoy, Dogan, Kaurav, Rahul P.S., 2022-02-11 This comprehensive Handbook offers an overview of current research on the use of social media within the tourism industry, investigating a range of social media practices and proposing strategies to address key challenges faced by tourist destinations and operators. |
| benefits of sentiment analysis: Cognitive Analytics: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2020-03-06 Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries, including business and healthcare. It is necessary to develop specific software programs that can analyze and interpret large amounts of data quickly in order to ensure adequate usage and predictive results. Cognitive Analytics: Concepts, Methodologies, Tools, and Applications provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques. It also examines the incorporation of pattern management as well as decision-making and prediction processes through the use of data management and analysis. Highlighting a range of topics such as natural language processing, big data, and pattern recognition, this multi-volume book is ideally designed for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, software engineers, IT specialists, and academicians. |
| benefits of sentiment analysis: Transforming Organizations Through Flexible Systems Management P.K. Suri, Rajan Yadav, 2019-08-23 The book focuses on key emerging areas concerning flexible systems management as an approach for transforming organizations. It is divided into three parts, discussing Enterprise Flexibility and Performance Management; Transformational Strategies and Organizational Competitiveness; and Supply Chain Flexibility. Part I addresses the integration aspects of learning, innovation, and entrepreneurship for organizational success, performance gains through cross-border acquisitions, flexibility measurement, and organizational competitiveness, impact of disinvestment, employability gaps and sustainable growth. Part II then examines risk governance structure, supporting culture, channel collaboration, waste management, IT-based process re-engineering, HR flexibility and adoption of big data as transformational strategies. Lastly, the third part investigates the development of a framework for a green flexible manufacturing system, measuring the effect of supply chain design on firm performance, exploring and ranking logistics service providers’ best practices, and exploring the relationship between optimism and career planning in the context of manufacturing sector, and analyzes customers’ emotional engagement and their inclinations towards the brand. The concept of flexibility is a common thread running through the three parts. The book is supported by both quantitative- and qualitative-based research as well as case applications relating to different areas of government and profit and not for profit organizations. Written by leading academics and practitioners, it is a useful resource for management students, scholars, consultants and practicing managers in both government and corporate sectors. |
| benefits of sentiment analysis: Pathways Between Social Science and Computational Social Science Tamás Rudas, Gábor Péli, 2021-01-22 This volume shows that the emergence of computational social science (CSS) is an endogenous response to problems from within the social sciences and not exogeneous. The three parts of the volume address various pathways along which CSS has been developing from and interacting with existing research frameworks. The first part exemplifies how new theoretical models and approaches on which CSS research is based arise from theories of social science. The second part is about methodological advances facilitated by CSS-related techniques. The third part illustrates the contribution of CSS to traditional social science topics, further attesting to the embedded nature of CSS. The expected readership of the volume includes researchers with a traditional social science background who wish to approach CSS, experts in CSS looking for substantive links to more traditional social science theories, methods and topics, and finally, students working in both fields. |
| benefits of sentiment analysis: Thick Big Data Dariusz Jemielniak, 2020 Thick Big Data presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. These tools are critical for students and researchers in the social sciences to successfully build mixed-methods approaches. |
| benefits of sentiment analysis: Exceeding Expectations Ron Legarski, Patrick Oborn, Ned Hamzic, Steve Sramek, Bryan Clement, Mark Prudell, Mark Radford, 2024-09-22 Exceeding Expectations: Mastering Customer Experience in the Modern Marketplace is a comprehensive guide for understanding and elevating Customer Experience (CX). This book explores the essential components of CX, from its evolution and the importance of customer psychology to designing seamless digital and omnichannel strategies. It delves into advanced metrics, data analytics, and the role of technology in transforming CX. With real-world case studies, the book offers actionable insights on how businesses can harness CX to drive loyalty, satisfaction, and long-term success. It’s a valuable resource for professionals looking to exceed customer expectations in today’s competitive market. |
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Sequential Models for Sentiment Analysis: A Comparative …
Keywords: Sentiment analysis · Sequence modeling · Word embedding · Machine learning algorithm · Deep learning algorithm 1 Introduction Sentiment analysis is an open research area …
Exploring emotional dimensions in educational data: A …
neutrosophic sentiment analysis approach University of New Mexico Exploring emotional dimensions in educational data: A neutrosophic sentiment analysis approach. Joffre Paladines …
Large Language Models Meet Text-Centric Multimodal …
Text-based sentiment analysis is a crucial research task in the field of natural language processing, aiming at automatically uncovering the underlying attitude that we hold towards …
Ratings revisited: Textual analysis for better risk management
Applications and benefits Sentiment analysis and the information it yields can improve banks’ credit-rating models, and it can also help with two other important tasks. In rating models, …
DeepMLF: Multimodal language model with learnable tokens …
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis Efthymios …
Twitter Sentiment Analysis on the Cryptocurrency Market
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Multimodal Transformer for Sentiment Analysis YANGMIN LI, RUIQI ZHU, and WENGEN LI∗, Department of Computer Science and Technology, Tongji University, China Multimodal …
Mamadou MBAYE, Iba Der Thiam University of Thies
Among other benefits, sentiment analysis algorithms detect patterns in customer feedback and identify areas for improvement. Moreover, automation technology allows marketers to rapidly …
Sentiment Analysis Methods, Applications and Challenges
A summary of the benefits and challenges of sentiment analysis to keep you up to date with current trend research. Each method is compared with its advantages and disadvantages, …
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benefits. Sentiment analysis is a very interesting topic and it is the process of the deriving sentiment of a specific sentence or statement. The reviews for the product are classified using …
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The emotional analysis is sometimes merged with sentiment analysis in which case multiple tuples come into use. Sentiment analysis basically involves classification, but the content that …
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Apr 27, 2023 · What is Sentiment Analysis and Its Benefits Sentiment analysis [8] is the computer evaluation of a per-son’s feelings, emotions, or attitudes toward a product, service, or other …
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that improved sentiment analysis by allowing models to perform complex operations of sentiment classification more effectively [2]. Fig.1. Sentiment analysis showing different emotions The …
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benefits of expanding sentiment types and dataset size. ... sentiment analysis by proposing a method for learning word vectors to perform sentiment classification (Maas et al. 2011).
Meme Similarity and Emotion Detection using Multimodal …
Next, [28] applied multimodal sentiment analysis for memes, using self-supervised image in-painting to im-prove understanding of missing pixels. This work aims ... The sentiment analysis …
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Sentiment analysis is a field of research that examines public perceptions, emotions, reviews, mentalities, and thoughts toward enterprises in order to determine the conduct of subjective …
Sentiment analysis of news videos about artificial …
Sentiment Analysis (SA). In the dictionary-based SA method implemented, consumer/follower comments were classified as positive, neutral, and negative
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LS in text sentiment classification. Subsequently, in Section 4, we present the experimental results on eight sentiment analysis datasets. Finally, Section 5 provides a discussion and …
Sentiment Analysis in Facebook using Machine Learning …
Applying sentiment analysis over big data leads to a lot of insights and business benefits. Sentiment analysis, opinion mining or emotion detection is the process of extracting sentiment …
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Reviewing Benefits Of Sentiment Analysis: Unlocking the Spellbinding Force of Linguistics In a fast-paced world fueled by information and interconnectivity, the spellbinding force of linguistics …
Lexicon Based Approaches to Sentiment Analysis of Spanish …
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A Hybrid Deep Learning Approach for Sentiment Analysis …
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PL-FGSA: A Prompt Learning Framework for Fine-Grained …
2.1 Fine-Grained Sentiment Analysis Fine-grained sentiment analysis (FGSA) is a specialized task in sentiment analysis that seeks to determine the polarity of opinions with respect to …
Integrating Sentiment Analysis and Quality Function …
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Ratings revisited: Textual analysis for better risk management
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Dec 27, 2024 · What is Sentiment Analysis? Sentiment analysis is a way to find out if a piece of writing expresses positive feelings, negative feelings, or neither. It helps researchers …
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237 as customer reviews and social media posts. The close ties between sentiment analysis and terms like "natural language processing" (NLP), "opinion mining," and "text mining"
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