Text Analysis

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.

This Element provides a basic introduction to sentiment analysis, aimed at helping students and professionals in corpus linguistics to understand what sentiment analysis is, how it is conducted, and where it can be applied. It begins with a definition of sentiment analysis and a discussion of the domains where sentiment analysis is conducted and used the most. Then, it introduces two main methods that are commonly used in sentiment analysis known as supervised machine-learning and unsupervised learning (or lexicon-based) methods, followed by a step-by-step explanation of how to perform sentiment analysis with R. The Element then provides two detailed examples or cases of sentiment and emotion analysis, with one using an unsupervised method and the other using a supervised learning method.

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 existingmethods 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.