Social networks represent an emerging challenging sector where the natural language expressions of people can be easily reported through short but meaningful text messages. Key information that can be grasped from social environments relates to the polarity of text messages (ie, positive, negative, or neutral). In this chapter we present a literature review regarding polarity classification in social networks, by distinguishing between supervised, unsupervised, and semisupervised machine learning models. In particular, the most recent advancements of the state of the art are presented, focusing on the real nature of the messages that are actually provided in an informal and networked environment.

Fersini, E. (2017). Sentiment Analysis in Social Networks: A Machine Learning Perspective. In Sentiment Analysis in Social Networks (pp. 91-111). Elsevier Inc. [10.1016/B978-0-12-804412-4.00006-1].

Sentiment Analysis in Social Networks: A Machine Learning Perspective

Fersini, E
2017

Abstract

Social networks represent an emerging challenging sector where the natural language expressions of people can be easily reported through short but meaningful text messages. Key information that can be grasped from social environments relates to the polarity of text messages (ie, positive, negative, or neutral). In this chapter we present a literature review regarding polarity classification in social networks, by distinguishing between supervised, unsupervised, and semisupervised machine learning models. In particular, the most recent advancements of the state of the art are presented, focusing on the real nature of the messages that are actually provided in an informal and networked environment.
Capitolo o saggio
Sentiment Analysis; Social Networks; Machine Learning; Language; Relationships
English
Sentiment Analysis in Social Networks
2017
9780128044124
Elsevier Inc.
91
111
Fersini, E. (2017). Sentiment Analysis in Social Networks: A Machine Learning Perspective. In Sentiment Analysis in Social Networks (pp. 91-111). Elsevier Inc. [10.1016/B978-0-12-804412-4.00006-1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/118109
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