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.
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