Chinese Sentiment Analysis Based on CNN-BiLSTM-Attention Model

With the widespread proliferation of the Internet, individuals are increasingly inclined to express their opinions and comments on various matters online. This paper will discuss the feasibility of applying the Attention-Based CNN-BiLSTM model (ABCBM) to Chinese sentiment analysis. In order to demonstrate that the model can effectively adapt to the task, it is essential to select a high-quality dataset. This paper has chosen the WeiboSenti100k dataset, which contains 100,000 comments posted on Weibo. After splitting the dataset into train texts and test texts randomly, this study uses the model to capture the Semantic information inside the texts. The result of the experiment shows that this model can manage the Chinese sentiment analysis well, achieving 98% accuracy, 97% recall and 98% F1 score in classifying texts as positive and negative, which proves the capacity that ABCBM holds in clarifying the sentiment inside the Chinese language.