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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

Sentiment Analysis in Social Media

verfasst von : Georgios Paltoglou

Erschienen in: Online Collective Action

Verlag: Springer Vienna

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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.

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Nächstes Kapitel Emotion Analysis on Social Media: Natural Language Processing Approaches and Applications
Fußnoten
3
For more information about the terminology in the field, we refer the interested reader to chapter 1.5 of Pang and Lee (2008).
 
4
WordNet is a lexical database of English words which in addition to standard definitions also provides semantic relations between words.
 
7
API stands for “Application Protocol Interface” and usually provides methods for accessing the content of services or software through programming techniques. A guide to the Twitter API can be found here: https://​dev.​twitter.​com/​
 
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Metadaten
Titel
Sentiment Analysis in Social Media
verfasst von
Georgios Paltoglou
Copyright-Jahr
2014
Verlag
Springer Vienna
Buch
Online Collective Action

Print ISBN: 978-3-7091-1339-4

Electronic ISBN: 978-3-7091-1340-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-7091-1340-0_1