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Naive Features for Sentiment Analysis on Mexican Touristic Opinions Texts

This paper presents a simple approach to extract naive features to represent and classify tourists’ opinions in Mexican places to participate in the Rest-Mex 2021 evaluation forum. The proposed approach consists of extracting 15 simple features. Then, various classification algorithms were used to evaluate the quality of these features, such as SVM, KNN, Decision Tree, Random Forest, and Naive Bayes. A weighting scheme was also proposed to obtain the best combination between algorithms and features, where it turned out that the best algorithm for this set of features was KNN with seven neighbors. Of these features, the best turned out to be what had to do with the length of words and characters and the number of stop words. With this approach, 0.76 of MAE was obtained, obtaining 10th place out of 15 teams, which considering the simplicity of this solution, makes it an acceptable result.