Sentiment Analysis for polarity classification on microblogs is generally based on the assumption that texts are independent and identically distributed (i.i.d). Although these methods are aimed at handling the complex characteristics of natural language, usually they do not consider microblogs as networked data. Early approaches for overcoming this limitation consist in exploiting friendship relationships, since connected users may be more likely to hold similar opinions (Homophily and Social Influence). However, the assumption about the friendship relations does not reflect the real world, where two connected users could have different opinions about the same topic. In order to overcome these shortcomings, we propose a semi-supervised framework that estimates user polarities about a given topic by combining post contents and weighted approval relations, which are intended to better represent the contagion on social networks. The experimental investigation reveals that incorporating approval relations can lead to statistically significant improvements over the performance of complex supervised classifiers based only on textual features. © Springer International Publishing Switzerland 2013.

Pozzi, F., Maccagnola, D., Fersini, E., Messina, V. (2013). Enhance user-level Sentiment Analysis on microblogs with approval relations. In Proceeding of the 13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013 (pp.133-144) [10.1007/978-3-319-03524-6_12].

Enhance user-level Sentiment Analysis on microblogs with approval relations

POZZI, FEDERICO ALBERTO;MACCAGNOLA, DANIELE;FERSINI, ELISABETTA;MESSINA, VINCENZINA
2013

Abstract

Sentiment Analysis for polarity classification on microblogs is generally based on the assumption that texts are independent and identically distributed (i.i.d). Although these methods are aimed at handling the complex characteristics of natural language, usually they do not consider microblogs as networked data. Early approaches for overcoming this limitation consist in exploiting friendship relationships, since connected users may be more likely to hold similar opinions (Homophily and Social Influence). However, the assumption about the friendship relations does not reflect the real world, where two connected users could have different opinions about the same topic. In order to overcome these shortcomings, we propose a semi-supervised framework that estimates user polarities about a given topic by combining post contents and weighted approval relations, which are intended to better represent the contagion on social networks. The experimental investigation reveals that incorporating approval relations can lead to statistically significant improvements over the performance of complex supervised classifiers based only on textual features. © Springer International Publishing Switzerland 2013.
paper
Computer Science (all); Theoretical Computer Science
English
International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013
2013
Proceeding of the 13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013
9783319035239
2013
8249
133
144
none
Pozzi, F., Maccagnola, D., Fersini, E., Messina, V. (2013). Enhance user-level Sentiment Analysis on microblogs with approval relations. In Proceeding of the 13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013 (pp.133-144) [10.1007/978-3-319-03524-6_12].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/59461
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