Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media.

Processing Affect in Social Media: A Comparison of Methods to Distinguish Emotions in Tweets

MEO, Rosa;SULIS, EMILIO
2017-01-01

Abstract

Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media.
2017
17
1
1
25
http://dl.acm.org/citation.cfm?id=2996187
Probabilistic methods; lexical approach; latent factors
Meo, Rosa; Sulis, Emilio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1623642
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