Overview of Rest-Mex at IberLEF 2022: Recommendation System, Sentiment Analysis and Covid Semaphore Prediction for Mexican Tourist Texts

• Published in: Procesamiento del Lenguaje Natural Vol. 69; p. 289
• Main Authors: Álvarez-Carmona, Miguel Á, Díaz-Pacheco, Ángel, Aranda, Ramón, Rodríguez-González, Ansel Y, Fajardo-Delgado, Daniel, Guerrero-Rodríguez, Rafael, Bustio-Martínez, Lázaro
• Format: Journal Article
• Language: English
• Published: Jaén Sociedad Española para el Procesamiento del Lenguaje Natural 01.09.2022
• Subjects: Attitudes; Data mining; Natural language processing; Polarity; Recommender systems; Sentiment analysis; Spanish language; Texts; Tourism; Websites; Mexico
• ISSN: 1135-5948; 1989-7553
• DOI: 10.26342/2022-69-26

This paper presents the framework and results from the Rest-Mex task at IberLEF 2022. This task considered three tracks: Recommendation System, Sentiment Analysis and Covid Semaphore Prediction, using texts from Mexican touristic places. The Recommendation System task consists in predicting the degree of satisfaction that a tourist may have when recommending a destination of Nayarit, Mexico, based on places visited by the tourists and their opinions. On the other hand, the Sentiment Analysis task predicts the polarity of an opinion issued and the attraction by a tourist who traveled to the most representative places in Mexico. We have built corpora for both tasks considering Spanish opinions from the TripAdvisor website. As a novelty, the Covid Semaphore Prediction task aims to predict the color of the Mexican Semaphore for each state, according to the Covid news in the state, using data from the Mexican Ministry of Health. This paper compares and discusses the participants’ results for all three tacks.