Sentiment Analysis on Monolingual, Multilingual and Code-Switching Twitter Corpora

Vilares, David; Alonso, Miguel A.; Gómez-Rodríguez, Carlos

Use this link to cite:

http://hdl.handle.net/2183/34995

Sentiment Analysis on Monolingual, Multilingual and Code-Switching Twitter Corpora

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Vilares_David_2015_Sentiment_Analysis_on_Monolingual_Multilingual_and_Code_Switching_Twitter_Corpora.pdf (208.31 KB)

Identifiers

URI: http://hdl.handle.net/2183/34995

DOI: 10.18653/v1/W15-2902

Publication date

2015

Authors

Vilares, David

Alonso, Miguel A.

Gómez-Rodríguez, Carlos

Bibliographic citation

David Vilares, Miguel A. Alonso, and Carlos Gómez-Rodríguez. 2015. Sentiment Analysis on Monolingual, Multilingual and Code-Switching Twitter Corpora. In Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 2–8, Lisboa, Portugal. Association for Computational Linguistics.

Abstract

[Abstract]: We address the problem of performing po- larity classification on Twitter over differ- ent languages, focusing on English and Spanish, comparing three techniques: (1) a monolingual model which knows the language in which the opinion is written, (2) a monolingual model that acts based on the decision provided by a language iden- tification tool and (3) a multilingual model trained on a multilingual dataset that does not need any language recognition step. Results show that multilingual models are even able to outperform the monolingual models on some monolingual sets. We introduce the first code-switching corpus with sentiment labels, showing the robust- ness of a multilingual approach.