A Simple Approach to Multilingual Polarity Classification in Twitter

TL;DR

This paper introduces a straightforward multilingual sentiment analysis framework for Twitter, serving as a baseline for contests and outperforming existing methods in several languages.

Contribution

It presents a simple, easy-to-implement multilingual approach for Twitter sentiment analysis that performs well across multiple languages and benchmarks.

Findings

Achieves high rankings in SemEval, TASS, and SENTIPOLC contests.

Outperforms existing results in several other languages.

Provides a practical baseline for future multilingual sentiment analysis systems.

Abstract

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 starting point to build new sentiment analysis systems. We compare our approach in eight different languages, three of them have 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.