Fuzzy Rough Nearest Neighbour Methods for Aspect-Based Sentiment Analysis

Fine-grained sentiment analysis, known as Aspect-Based Sentiment Analysis (ABSA), establishes the polarity of a section of text concerning a particular aspect. Aspect, sentiment, and emotion categorisation are the three steps that make up the configuration of ABSA, which we looked into for the dataset of English reviews. In this work, due to the fuzzy nature of textual data, we investigated machine learning methods based on fuzzy rough sets, which we believe are more interpretable than complex state-of-the-art models. The novelty of this paper is the use of a pipeline that incorporates all three mentioned steps and applies Fuzzy-Rough Nearest Neighbour classification techniques with their extension based on ordered weighted average operators (FRNN-OWA), combined with text embeddings based on transformers. After some improvements in the pipeline's stages, such as using two separate models for emotion detection, we obtain the correct results for the majority of test instances (up to 81.4%) for all three classification tasks. We consider three different options for the pipeline. In two of them, all three classification tasks are performed consecutively, reducing data at each step to retain only correct predictions, while the third option performs each step independently. This solution allows us to examine the prediction results after each step and spot certain patterns. We used it for an error analysis that enables us, for each test instance, to identify the neighbouring training samples and demonstrate that our methods can extract useful patterns from the data. Finally, we compare our results with another paper that performed the same ABSA classification for the Dutch version of the dataset and conclude that our results are in line with theirs or even slightly better.

Automated summary

Fine-grained sentiment analysis involves determining the polarity of a text section based on a particular aspect, using aspect, sentiment, and emotion categorisation. In this study, we propose a pipeline that integrates these three steps and applies Fuzzy-Rough Nearest Neighbour classification techniques with text embeddings based on transformers. After improvements, our pipeline achieves accurate results for the majority of test instances (up to 81.4%) in all three classification tasks.