Sentiment Analysis of Chinese Product Reviews Based on Fusion of DUAL-Channel BiLSTM and Self-Attention

Ye Yuan (), Wang Wang, Guangze Wen, Zikun Zheng and Zhemin Zhuang
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Ye Yuan: College of Engineering, Shantou University, Shantou 515063, China
Wang Wang: College of Engineering, Shantou University, Shantou 515063, China
Guangze Wen: College of Engineering, Shantou University, Shantou 515063, China
Zikun Zheng: College of Engineering, Shantou University, Shantou 515063, China
Zhemin Zhuang: College of Engineering, Shantou University, Shantou 515063, China

Future Internet, 2023, vol. 15, issue 11, 1-19

Abstract: Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the onlineshopping10cats dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively.

Keywords: natural language processing; sentiment classification; BiLSTM; self-attention; scalable multi-channel (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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