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

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Název: Sentiment Analysis of Chinese Product Reviews Based on Fusion of DUAL-Channel BiLSTM and Self-Attention.
Autoři: Yuan, Ye, Wang, Wang, Wen, Guangze, Zheng, Zikun, Zhuang, Zhemin
Zdroj: Future Internet; Nov2023, Vol. 15 Issue 11, p364, 19p
Témata: SENTIMENT analysis, CHINESE language, PRODUCT reviews, FEATURE extraction, CONSUMER education, NATURAL language processing, BINARY codes
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:19995903
DOI:10.3390/fi15110364