Building an enhanced sentiment classification framework based on natural language processing

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-219278