Overview of Rest-Mex at IberLEF 2023: Research on Sentiment Analysis Task for Mexican Tourist Texts

Miguel Ángel Álvarez-Carmona, Ángel Díaz-Pacheco, Ramón Aranda, Ansel Yoan Rodríguez-González, Victor Muñiz-Sánchez, Adrián Pastor López-Monroy, Fernando Sánchez-Vega, Lázaro Bustio-Martínez


This paper presents the framework and results of the Rest-Mex task at IberLEF 2023, focusing on sentiment analysis and text clustering of tourist texts. The study primarily focuses on texts related to tourist destinations in Mexico, although this edition included data from Cuba and Colombia for the first time. The sentiment analysis task aims to predict the polarity of opinions expressed by tourists, classifying the type of place visited, whether it's a tourist attraction, hotel, or restaurant, as well as the country it is located in. On the other hand, the text clustering task aims to classify news articles related to tourism in Mexico. For both tasks, corpora were built using Spanish opinions extracted from TripAdvisor and news articles from Mexican media. This article compares and discusses the results obtained by the participants in both sub-tasks. Additionally, a method is proposed to measure the easiness of a multi-class text classification corpus, along with an approach for system selection in a possible late fusion scheme.

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