Aspect Category Detection

Abstract

The major subtask of Sentiment analysis based on aspects (ABSA) is the category of aspectdetection (ACD). Due to the subjectivity inherent in categorizing, as well as the occurrence of overlapping classes, it is a difficult challenge to solve. Rule-based techniques, as well as other machine learning approaches, have been used to tackle ACD, and a majority of them are statistical behavior. We employed an association rulebased method in this article. We developed a mixed principle strategy that incorporates both association rule mining and semantics associations to address the statistical limitations of association rules. We employed the concept of word-embed for semantic linkages. The experiments were carried out using the SemEval dataset, which is a standardized set of data for categorizing features industry. We discovered how semantic connections could help to enhance classification accuracy by complementing statistical associations. The proposed method outperforms several statistical methods.