Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts

TL;DR

This paper explores adapting an unsupervised, rule-based sentiment analysis method to Universal Dependencies for Mexican tourist texts, demonstrating improved interpretability, robustness, and usability over other heuristic approaches.

Contribution

It introduces an UD-adapted unsupervised rule-based approach for sentiment analysis, enhancing interpretability and robustness compared to existing heuristic methods.

Findings

Significant improvement over other unsupervised approaches

Effective use of syntactic rules for negation and modification

Potential for future enhancements with modality features and disambiguation

Abstract

This paper summarizes the results of experimenting with Universal Dependencies (UD) adaptation of an Unsupervised, Compositional and Recursive (UCR) rule-based approach for Sentiment Analysis (SA) submitted to the Shared Task at Rest-Mex 2023 (Team Olga/LyS-SALSA) (within the IberLEF 2023 conference). By using basic syntactic rules such as rules of modification and negation applied on words from sentiment dictionaries, our approach exploits some advantages of an unsupervised method for SA: (1) interpretability and explainability of SA, (2) robustness across datasets, languages and domains and (3) usability by non-experts in NLP. We compare our approach with other unsupervised approaches of SA that in contrast to our UCR rule-based approach use simple heuristic rules to deal with negation and modification. Our results show a considerable improvement over these approaches. We discuss future improvements of our results by using modality features as another shifting rule of polarity and word disambiguation techniques to identify the right sentiment words.