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 …

Automatic discovery of underlying themes in a document collection is a valuable task across
many disciplines. Advanced techniques can be challenging for non-experts in data science …

The relationship between cities and tourism is complex. It is far more than tourism occurring
in an urban environment. As argued by Ashworth & Page (2011), there is an urbanicity …

In this study, we developed a single Beto classification model capable of predicting 45
different classes that describes the polarity, type, and country of tourist opinions in Spanish …

This article presents a proposal for the treatment of an unbalanced tourist database with
emphasis on minority classes for its classification, in this case, one based on BERT, called …

Sentiment analysis and thematic unsupervised classification of tourist texts have gained
importance in recent years. Rest-Mex 2023 proposes two tasks: sentiment analysis and …

This paper addresses the challenge of enriching a Spanish sentiment analysis corpus with
minority instances. Existing sentiment analysis resources for Spanish suffer from a …

In this work we present a solution to the Thematic Unsupervised Classification, a track
presented in this year Rest-Mex competition, in which four topic-based groups are to be …

This work describes and presents the methodology of the IIMAS-UNAM team followed in the
sentiment analysis task in the REST-MEX 2023 competition on tourist reviews. It shows the …

This paper investigates the feasibility of using smaller amounts of data to accurately classify
tourist opinions in Spanish. The classification of tourist reviews is important for businesses in …