期刊
JOURNAL OF RETAILING AND CONSUMER SERVICES
卷 77, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jretconser.2023.103605
关键词
Consumer preference; Fine-grained sentiment analysis; Deep learning; Chinese automotive reviews
类别
This study proposes a new framework that combines graph neural networks and fine-grained sentiment analysis to generate opinion pairs, effectively revealing consumer preferences in automotive reviews and providing marketing strategies.
Unveiling consumer preferences in online reviews is receiving increasing attention. While most existing ap-proaches for consumer preferences have achieved significant improvements, fine-grained sentiment is rarely considered. Fine-grained sentiment analysis involves several essential tasks, such as aspect-opinion recognition, and sentiment orientation analysis. However, existing methods cannot effectively generate an opinion pair, especially when dealing with Chinese automotive reviews. In this paper, we propose a joint course-and fine-grained sentiment analysis of preferences, a new framework for opinion pair generation using graph neural networks (GCN), which optimizes model performance based on aspect-wise sentiment information, as well as our experiments on the course-and fine-grained tasks. Our graph-based multi-grained convolution (CMGC) model outperforms all baselines by at least 1% accuracy in coarse-grained tasks. The results in the fine-grained task are significantly better than the baseline, surpassing the previous state-of-the-art by 1.33% and 3.88% in R and R@1, respectively. Our results can effectively reveal consumer preferences from automotive reviews, which provides business managers with specific marketing strategies.
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