Building an enhanced sentiment classification framework based on natural language processing

Issue title: Soft Computing Applications

Guest editors: Valentina Emilia Balas

Article type: Research Article

Authors: Tran, Thien Khai^{a; b; c; *} | Dinh, Hoa Minh^c | Phan, Tuoi Thi^{a; b}

Affiliations: [a] Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam | [b] Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam | [c] Ho Chi Minh City University of Foreign Languages-Information Technology (HUFLIT), Ho Chi Minh City, Vietnam

Correspondence: [*] Corresponding author. Thien Khai Tran. E-mail: [email protected]; [email protected].

Abstract: 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.

Keywords: Sentiment analysis, sentiment classification, natural language processing, bag-of-words, word2vec, text representation

DOI: 10.3233/JIFS-219278

Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1771-1777, 2022