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