Sentiment analysis and opinion mining have been acquiring a crucial role in both commercial and research applications because of their possible applicability to several different fields. Therefore a large number of companies have included the analysis of opinions and sentiments of customers as part of their mission. One of the most interesting applications of these approaches involves the automatic analysis of social network messages, on the basis of the feelings and emotions conveyed. This chapter aims to relate the most recent state-of-the-art sentiment-based techniques and tools to the affective characterization that may be inferred from social networks. The main result consists of a review of the most interesting methods employed to compare and classify messages on social media platforms and a description of advanced tools in this area.

Sentic Computing for Social Network Analysis

Bisio, F.;Oneto, L.;Cambria, E.
2016

Abstract

Sentiment analysis and opinion mining have been acquiring a crucial role in both commercial and research applications because of their possible applicability to several different fields. Therefore a large number of companies have included the analysis of opinions and sentiments of customers as part of their mission. One of the most interesting applications of these approaches involves the automatic analysis of social network messages, on the basis of the feelings and emotions conveyed. This chapter aims to relate the most recent state-of-the-art sentiment-based techniques and tools to the affective characterization that may be inferred from social networks. The main result consists of a review of the most interesting methods employed to compare and classify messages on social media platforms and a description of advanced tools in this area.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/881482
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