Aspect category detection using statistical and semantic association

Aspect category detection (ACD) is an important subtask of aspect-based sentiment analysis (ABSA). It is a challenging problem due to subjectivity involved in categorization, as well as the existence of overlapping classes. Among various approaches that have been applied to ACD include rule-based approaches along with other machine learning approaches, and most of them are statistical in nature. In this article, we have used an association rule-based approach. To deal with the statistical limitation of association rules, we proposed a hybridized rule-based approach that combines association rules with the semantic association. For semantic associations, we have used the notion of word-embeddings. Experiments were performed on SemEval dataset, a standard benchmark dataset for aspect categorization in the restaurant domain. We observed that semantic associations can complement statistical association and improve the accuracy of classification. The proposed method performs better than several state-of-the-art methods.