Experimenting with UD Adaptation of an Unsupervised Rule-based Approach
  for Sentiment Analysis of Mexican Tourist Texts
Olga Kellert Mahmud Uz Zaman Nicholas Hill Matlis Carlos Gómez-Rodríguez
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.