Please use this identifier to cite or link to this item: http://centrogeo.repositorioinstitucional.mx/jspui/handle/1012/244
A simple approach to multilingual polarity classification in twitter
Eric Tellez
SABINO MIRANDA JIMENEZ
Mario Graff
Daniela Moctezuma
Ranyart Rodrigo Suarez Ponce de Leon
Oscar Sánchez Siordia
En Embargo
16-07-2019
Atribución-NoComercial-SinDerivadas
https://doi.org/10.1016/j.patrec.2017.05.024
Multilingual sentiment analysis
Error-robust text representations
Opinion mining
Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations and errors introduced by the people generating content. In this contribution, our aim is to provide a simple to implement and easy to use multilingual framework, that can serve as a baseline for sentiment analysis contests, and as a starting point to build new sentiment analysis systems. We compare our approach in eight different languages, three of them correspond to important international contests, namely, SemEval (English), TASS (Spanish), and SENTIPOLC (Italian). Within the competitions, our approach reaches from medium to high positions in the rankings; whereas in the remaining languages our approach outperforms the reported results.
Elsevier
15-07-2017
Artículo
Pattern Recognition Letters Volume 94, 15 July 2017, Pages 68-74
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INTELIGENCIA ARTIFICIAL
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