With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.

Despite the success of dichotomous sentiment analysis, it does not encompass the various emotional colors of users in reality, which can be more plentiful than a mere positive or negative association. Moreover, the complexity and imbalanced nature of Chinese text presents a formidable obstacle to overcome. To address prior inadequacies, the three-classification method is employed and a novel AB-CNN model is proposed, incorporating an attention mechanism, BiLSTM, and a CNN. The proposed model was tested on a public e-commerce dataset and demonstrated a superior performance compared to existing classifiers. It utilizes a word vector model to extract features from sentences and vectorize them. The attention layer is used to calculate the weighted average attention of each text, and the relevant representation is obtained. BiLSTM is then employed to read the text information from both directions, further enhancing the emotional level. Finally, softmax is used to classify the emotional polarity.