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Sentiment Analysis in Social Media

  • Georgios PaltoglouEmail author
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Sentiment Analysis deals with the detection and analysis of affective content in written text. It utilizes methodologies, theories, and techniques from a diverse set of scientific domains, ranging from psychology and sociology to natural language processing and machine learning. In this chapter, we discuss the contributions of the field in social media analysis with a particular focus in online collective actions; as these actions are typically motivated and driven by intense emotional states (e.g., anger), sentiment analysis can provide unique insights into the inner workings of such phenomena throughout their life cycle. We also present the state of the art in the field and describe some of its contributions into understanding online collective behavior. Lastly, we discuss significant real-world datasets that have been successfully utilized in research and are available for scientific purposes and also present a diverse set of available tools for conducting sentiment analysis.

Keywords

Online Community Opinion Mining Sentiment Analysis Affective Content Forum Post 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  1. 1.School of TechnologyUniversity of WolverhamptonWolverhamptonUK

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