Building an enhanced sentiment classification framework based on natural language processing

• Published in: Journal of intelligent & fuzzy systems 2022-01, Vol.43 (2), p.1771-1777
• Main Authors: Tran, Thien Khai, Dinh, Hoa Minh, Phan, Tuoi Thi
• Format: Article
• Language: English
• Subjects: Classification; Data mining; Natural language processing; Representations; Semantics; Sentiment analysis; Social networks
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-219278

Sentiment classification is one of the major tasks of natural language processing (NLP) and has gained much attention by researchers and businesses in recent years. However, the semantics of the social networking language is becoming increasingly complex and unpredictable, affecting the accuracy of the associated NLP systems. In this paper, we propose a hybrid sentiment analysis (SA) framework that classifies the opinions of Vietnamese reviews into one of two types: positive or negative. The special feature of the proposed framework is that it is built on a combination of three different text representation models that focus on analyzing social media network language characteristics. Our system achieved an accuracy score of 81.54% on the test set, which is better than other strategies. Based on the experimental results, this work proves that the choice of text representation model determines the performance of the system.