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

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

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.

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.

This book constitutes the refereed proceedings of the 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008, held in Windsor, Canada, in May 2008. The 30 revised full papers presented together with 5 revised short papers were carefully reviewed and selected from 75 submissions. The papers present original high-quality research in all areas of Artificial Intelligence and apply historical AI techniques to modern problem domains as well as recent techniques to historical problem settings.

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.

The definitive book on Positioning Analysis — a powerful and sophisticated framework to help traders, investors and risk managers better understand commodity markets Positioning Analysis is a powerful framework to better understand commodity price dynamics, risk, and sentiment. It indicates what each category of trader is doing—what they are trading, how much they are trading and how they might behave under a variety of different circumstances. It is essential in isolating specific types of flow patterns, defining behavioral responses, measuring shifts in sentiment, and developing tools for better risk management. Advanced Positioning, Flow and Sentiment Analysis in Commodity Markets explains the fundamentals of Positioning Analysis and presents new concepts in Commodity Positioning Analytics. This invaluable guide helps readers recognize how certain types of positioning patterns can be used to develop models, indicators, and analyses that can be used to enhance performance. This updated second edition contains substantial new material, including analytics based on the analysis of flow, the decomposition of trading flows, trading activity in the Chinese commodity markets, and the inclusion of Newsflow into Positioning Analysis. Author: Mark J S Keenan, also covers the structure of positioning data, performance attribution of speculators, sentiment analysis and the identification of price risks and behavioral patterns that can be used to generate trading signals.. This must-have resource: Offers intuitive and accessible guidance to commodity market participants and risk managers at various levels and diverse areas of the market Provides a wide range of analytics that can be used directly or integrated into a variety of different commodity-related trading, investment, and risk management programs Features an online platform comprising a wide range of customizable, regularly-updated analytical tools Contains an abundance of exceptional graphics, charts, and illustrations Includes easy-to-follow instructions for building analytics. Advanced Positioning, Flow and Sentiment Analysis in Commodity Markets: Bridging Fundamental and Technical Analysis, 2nd Edition is an indispensable source of information for all types of commodity traders, investors, and speculators, as well as investors in other asset classes who look to the commodity markets for price information.