PELESent: Cross-domain polarity classification using distant supervision

TL;DR

This paper introduces PELESent, a cross-domain sentiment classification method for Portuguese that leverages distant supervision with emojis and emoticons, trained on nearly one million tweets, achieving competitive results across multiple domains.

Contribution

It extends distant supervision techniques to include emojis for Portuguese sentiment analysis, demonstrating domain-independent effectiveness and improved sentiment capture.

Findings

Achieved competitive results on five diverse corpora

Demonstrated domain independence of the approach

Showed emojis and emoticons effectively capture sentiment

Abstract

The enormous amount of texts published daily by Internet users has fostered the development of methods to analyze this content in several natural language processing areas, such as sentiment analysis. The main goal of this task is to classify the polarity of a message. Even though many approaches have been proposed for sentiment analysis, some of the most successful ones rely on the availability of large annotated corpus, which is an expensive and time-consuming process. In recent years, distant supervision has been used to obtain larger datasets. So, inspired by these techniques, in this paper we extend such approaches to incorporate popular graphic symbols used in electronic messages, the emojis, in order to create a large sentiment corpus for Portuguese. Trained on almost one million tweets, several models were tested in both same domain and cross-domain corpora. Our methods obtained very competitive results in five annotated corpora from mixed domains (Twitter and product reviews), which proves the domain-independent property of such approach. In addition, our results suggest that the combination of emoticons and emojis is able to properly capture the sentiment of a message.