Wang Jiajia


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Learnable Conjunction Enhanced Model for Chinese Sentiment Analysis
Zhao Bingfei | Zan Hongying | Wang Jiajia | Han Yingjie
Proceedings of the 22nd Chinese National Conference on Computational Linguistics

“Sentiment analysis is a crucial text classification task that aims to extract, process, and analyzeopinions, sentiments, and subjectivity within texts. In current research on Chinese text, sentenceand aspect-based sentiment analysis is mainly tackled through well-designed models. However,despite the importance of word order and function words as essential means of semantic ex-pression in Chinese, they are often underutilized. This paper presents a new Chinese sentimentanalysis method that utilizes a Learnable Conjunctions Enhanced Model (LCEM). The LCEMadjusts the general structure of the pre-trained language model and incorporates conjunctionslocation information into the model’s fine-tuning process. Additionally, we discuss a variantstructure of residual connections to construct a residual structure that can learn critical informa-tion in the text and optimize it during training. We perform experiments on the public datasetsand demonstrate that our approach enhances performance on both sentence and aspect-basedsentiment analysis datasets compared to the baseline pre-trained language models. These resultsconfirm the effectiveness of our proposed method. Introduction”