Sentiment analysis is the task of automatically determining from text the attitude, emotion, or some other affectual state of the author. This chapter summarizes the diverse landscape of tasks and applications associated with sentiment analysis. We outline key challenges stemming from the complexity and subtlety of language use, the prevalence of creative and non-standard language, and the lack of paralinguistic information, such as tone and stress markers. We describe automatic systems and datasets commonly used in sentiment analysis. We summarize several manual and automatic approaches to creating valence- and emotion-association lexicons. We also discuss preliminary approaches for sentiment composition (how smaller units of text combine to express sentiment) and approaches for detecting sentiment in figurative and metaphoric language—these are the areas where we expect to see significant work in the near future.

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Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

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.

A Practical Guide to Sentiment Analysis

A vast majority of the work in Sentiment Analysis has been on developing more accurate sentiment classifiers, usually involving supervised machine learning algorithms and a battery of features. Surveys by Pang and Lee (Found Trends Inf Retr 2(12):1135, 2008), Liu and Zhang (A survey of opinion mining and sentiment analysis. In: Aggarwal CC, Zhai C (eds) In: Mining text data. Springer, New York, pp 415463, 2012), and Mohammad (Mohammad Sentiment analysis: detecting valence, emotions, and other effectual states from text. In: Meiselman H (ed) Emotion measurement. Elsevier, Amsterdam, 2016b) give summaries of the many automatic classifiers, features, and datasets used to detect sentiment. In this chapter, we flesh out some of the challenges that still remain, questions that have not been explored sufficiently, and new issues emerging from taking on new sentiment analysis problems. We also discuss proposals to deal with these challenges. The goal of this chapter is to equip researchers and practitioners with pointers to the latest developments in sentiment analysis and encourage more work in the diverse landscape of problems, especially those areas that are relatively less explored.

Challenges in Sentiment Analysis

Recent advances in machine learning have led to computer systems that are humanlike in behavior. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behavior. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.

Sentiment analysis: Automatically detecting valence, emotions, and other affectual states from text

Sentiment analysis focuses upon automatic classiffication of a document's sentiment (and more generally extraction of opinion from text). Ways of expressing sentiment have been
shown to be dependent on what a document is about (domain-dependency). This complicates supervised methods for sentiment analysis which rely on extensive use of training data or linguistic resources that are usually either domain-specific or generic. Both kinds of resources prevent classiffiers from performing well across a range of domains, as this requires appropriate in-domain (domain-specific) data.

This thesis presents a novel unsupervised, knowledge-poor approach to sentiment analysis aimed at creating a domain-independent and multilingual sentiment analysis system.
The approach extracts domain-specific resources from documents that are to be processed, and uses them for sentiment analysis. This approach does not require any training corpora, large sets of rules or generic sentiment lexicons, which makes it domain- and languageindependent but at the same time able to utilise domain- and language-specific information.

The thesis describes and tests the approach, which is applied to diffeerent data, including customer reviews of various types of products, reviews of films and books, and news items; and to four languages: Chinese, English, Russian and Japanese. The approach is applied not only to binary sentiment classiffication, but also to three-way sentiment classiffication (positive, negative and neutral), subjectivity classifiation of documents and sentences, and to the extraction of opinion holders and opinion targets. Experimental results suggest that the approach is often a viable alternative to supervised systems, especially when applied to large document collections.

Unsupervised and knowledge-poor approaches to sentiment analysis

Public health events with major consequences are occurring globally Increasingly people are expressing their views on these events and government agencies' responses and policies online Recent years have seen significant interest in investigating methods to recognize favorable and unfavorable sentiments towards specific subjects, including public health opinions, from online natural language text However, most of these efforts are focused on English In this paper, we study Chinese opinion mining in the context of public health opinions We explore two complementary approaches--a Chinese opinionated word-based approach and a machine learning approach We also conduct related comparative analysis and discuss the important role Chinese NLP techniques play in polarity classification

Xueling Xin

Papers

Polarity Classification of Public Health Opinions in Chinese