© 2017

A Practical Guide to Sentiment Analysis

  • Erik Cambria
  • Dipankar Das
  • Sivaji Bandyopadhyay
  • Antonio Feraco

Part of the Socio-Affective Computing book series (SAC, volume 5)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay, Antonio Feraco
    Pages 1-10
  3. Bing Liu
    Pages 11-39
  4. Jiwei Li, Eduard Hovy
    Pages 41-59
  5. Saif M. Mohammad
    Pages 61-83
  6. Aditya Joshi, Pushpak Bhattacharyya, Sagar Ahire
    Pages 85-106
  7. Hongning Wang, ChengXiang Zhai
    Pages 107-134
  8. Vasudeva Varma, Litton J. Kurisinkel, Priya Radhakrishnan
    Pages 135-153
  9. Paolo Rosso, Leticia C. Cagnina
    Pages 155-171
  10. Federica Bisio, Claudia Meda, Paolo Gastaldo, Rodolfo Zunino, Erik Cambria
    Pages 173-188
  11. Back Matter
    Pages 189-196

About this book


This edited work presents studies and discussions that clarify the challenges and opportunities of sentiment analysis research. While sentiment analysis research has become very popular in the past ten years, most companies and researchers still approach it simply as a polarity detection problem. In reality, sentiment analysis is a ‘suitcase problem’ that requires tackling many natural language processing subtasks, including microtext analysis, sarcasm detection, anaphora resolution, subjectivity detection and aspect extraction. 

 In this book, the authors propose an overview of the main issues and challenges associated with current sentiment analysis research and provide some insights on practical tools and techniques that can be exploited to both advance the state of the art in all sentiment analysis subtasks and explore new areas in the same context. Readers will discover sentiment mining techniques that can be exploited for the creation and automated upkeep of review and opinion aggregation websites, in which opinionated text and videos are continuously gathered from the Web and not restricted to just product reviews, but also to wider topics such as political issues and brand perception.

The book also enables researchers to see how affective computing and sentiment analysis have a great potential as a sub-component technology for other systems. They can enhance the capabilities of customer relationship management and recommendation systems allowing, for example, to find out which features customers are particularly happy about or to exclude from the recommendations items that have received very negative feedbacks. Similarly, they can be exploited for affective tutoring and affective entertainment or for troll filtering and spam detection in online social communication.


Sentiment Analysis Opinion mining Natural language processing Text summarization Deception detection Knowledge representation and reasoning

Editors and affiliations

  • Erik Cambria
    • 1
  • Dipankar Das
    • 2
  • Sivaji Bandyopadhyay
    • 3
  • Antonio Feraco
    • 4
  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Computer Science and Engineering DepartmentJadavpur UniversityKolkataIndia
  3. 3.Computer Science and Engineering DepartmentJadavpur UniversityKolkataIndia
  4. 4.Fraunhofer IDM@NTUNanyang Technological UniversitySingaporeSingapore

Bibliographic information

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