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A Practical Guide to Sentiment Analysis

Part of the Socio-Affective Computing Book Series (volume 5)

Editors: Cambria, Erik; Das, Dipankar; Bandyopadhyay, Sivaji; Feraco, Antonio

DOI: 10.1007/978-3-319-55394-8

ISBN: 978-3-319-55392-4

Publisher: Springer International Publishing - Springer Journals

Apr 12, 2017

1 - 9 of 9 Chapters

Available

Affective Computing and Sentiment Analysis

Cambria, Erik; Das, Dipankar; Bandyopadhyay, Sivaji; Feraco, Antonio

Pages: 1 –10

[Understanding emotions is one of the most important aspects of personal development and growth and, as such, it is a key tile for the emulation of human intelligence. Besides being a important for the advancement of AI, emotion processing is also important for the closely related task of...
Available

Many Facets of Sentiment Analysis

Liu, Bing

Pages: 11 –39

[Sentiment analysis or opinion mining is the computational study of people's opinions, sentiments, evaluations, attitudes, moods, and emotions. It is one of the most active research areas in natural language processing, data mining, information retrieval, and Web mining. In recent years, its...
Available

Reflections on Sentiment/Opinion Analysis

Li, Jiwei; Hovy, Eduard

Pages: 41 –59

[The detection of expressions of sentiment in online text has become a popular Natural Language Processing application. The task is commonly defined as identifying the words or phrases in a given fragment of text in which the reader understands that the author expresses some person’s positive,...
Available

Challenges in Sentiment Analysis

Mohammad, Saif M.

Pages: 61 –83

[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...
Available

Sentiment Resources: Lexicons and Datasets

Joshi, Aditya; Bhattacharyya, Pushpak; Ahire, Sagar

Pages: 85 –106

[Sentiment lexicons and datasets represent the knowledge base that lies at the foundation of a SA system. In its simplest form, a sentiment lexicon is a repository of words/phrases labelled with sentiment. Similarly, a sentiment-annotated dataset consists of documents (tweets, sentences or longer...
Available

Generative Models for Sentiment Analysis and Opinion Mining

Wang, Hongning; Zhai, ChengXiang

Pages: 107 –134

[This chapter provides a survey of recent work on using generative models for sentiment analysis and opinion mining. Generative models attempt to model the joint distribution of all the relevant data with parameters that can be interpreted as reflecting latent structures or properties in the...
Available

Social Media Summarization

Varma, Vasudeva; Kurisinkel, Litton J.; Radhakrishnan, Priya

Pages: 135 –153

[Social media is an important venue for information sharing, discussions or conversations on a variety of topics and events generated or happening across the globe. Application of automated text summarization techniques on the large volume of information piled up in social media can produce...
Available

Deception Detection and Opinion Spam

Rosso, Paolo; Cagnina, Leticia C.

Pages: 155 –171

[In this chapter we first introduce the reader to the problem of deception detection in general, describing how lies may be detected automatically using different methods. Later we address the specific problem of deception detection in predatory communication. We make emphasis especially on those...
Available

Concept-Level Sentiment Analysis with SenticNet

Bisio, Federica; Meda, Claudia; Gastaldo, Paolo; Zunino, Rodolfo; Cambria, Erik

Pages: 173 –188

[SenticNet is a publicly available resource for opinion mining that exploits AI, linguistics, and psychology to infer the polarity associated with commonsense concepts and encode this in a semantic-aware representation. In particular, SenticNet uses dimensionality reduction to calculate the...

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