Design and Implementation of B-A-LSTM Chinese Text Sentiment Analysis Model

To address the limitations of existing text sentiment analysis models in effectively capturing semantic information and extracting features when processing Chinese text data, this paper proposes a B-A-LSTM-based model for Chinese text sentiment analysis. This model utilizes pretrained BERT as the underlying encoder. Through parameter tuning during experiments, it demonstrates strong applicability for Chinese text scenarios. The internal feature extraction mechanism of BERT ensures that the acquired features retain relatively intact semantic information. A self-attention mechanism is incorporated to enable deeper feature extraction. Furthermore, an LSTM network is selected due to its complex internal structure and unique memory function, which significantly enhances its feature extraction capability. Finally, DNNLinear is employed to classify the acquired features carrying sentiment tendencies. Through comparative experiments with existing deep learning text sentiment analysis algorithms, the results demonstrate the effectiveness of the proposed model.