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.

翻译：本文总结了在IberLEF 2023会议Rest-Mex 2023共享任务（Team Olga/LyS-SALSA）中，针对无监督、组合与递归（UCR）规则方法进行通用依赖（UD）适配的实验结果。该方法采用情感词典中单词的修饰与否定等基本句法规则，利用了无监督情感分析方法的三个优势：（1）情感分析的可解释性与可说明性，（2）跨数据集、语言及领域的鲁棒性，（3）非自然语言处理专家的易用性。我们将该方法与其他无监督情感分析方法进行对比——后者在处理否定与修饰时仅采用简单启发式规则，与本文基于UCR规则的方法形成差异。实验结果表明，本方法较同类方法有显著改进。文章进一步讨论了通过引入情态特征作为极性转移规则、以及词义消歧技术以识别正确情感词的未来改进方向。