Title

A simple approach to multilingual polarity classification in twitter

Author

Eric Tellez

SABINO MIRANDA JIMENEZ

Mario Graff

Daniela Moctezuma

Ranyart Rodrigo Suarez Ponce de Leon

Oscar Sánchez Siordia

Access level

Embargoed Access

Alternative identifier

doi: https://doi.org/10.1016/j.patrec.2017.05.024

Summary or description

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.

Publisher

Elsevier

Publish date

July 15, 2017

Publication type

Article

Publication version

Accepted Version

Format

application/pdf

Source

Pattern Recognition Letters Volume 94, 15 July 2017, Pages 68-74

Language

English

Audience

Students

Researchers

Teachers

Source repository

Repositorio Institucional de CENTROGEO

Downloads

3

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