Nowadays, social media networks generate a tremendous amount of social information from their users. To understand people’s views and sentimental tendencies on a commodity or an event timely, it is necessary to conduct text sentiment analysis on the views expressed by users. For the microblog comment data, it is always mixed with long and short texts, which is relatively complex. Especially for long text data, it contains a lot of content, and the correlation between words is more complex than that in short text. To study the sentiment classification of these mixed texts composed of long-text and short-text, this research proposes an optimized GloVe-CNN-BiLSTM-based sentiment analysis model. In this model, GloVe is used to vectorize words, and CNN is given to represent part space character. BiLSTM is used to build temporal relationship. Twitter’s comment data on COVID-19 is used as an experimental dataset. The results of the experiments suggest that this method can effectually identify the sentimental tendency of users’ online comments, and the accuracy of sentiment classification on complete-text, long-text, and short-text can achieve to 0.9565, 0.9509, and 0.9560, respectively, which is obviously higher than other deep learning models. At the same time, experiments show that this method has good field expansion.

Sentiment classification has become a significant research topic in natural language processing. As the most popular research method for sentiment classification, deep learning has been applied to various experimental datasets by many scholars and has achieved good results. Aiming at the problems of poor effect and insufficient accuracy of sentiment classification in the current vertical field, and to solve sentiment classification on Chinese mixed text, including both long text and short text, this work proposes an improved BiLSTM-Attention model that can extract features more effectively. The problem of insufficient dependence on long text is resolved by the Bi-directional Long Short-Term Memory (BiLSTM) model, and important information in the text is obtained by the attention mechanism. This study uses online shopping comment datasets for experiments and applies a multiclassification evaluation index to evaluate the model. Experiments support the proposed approach, the accuracy of sentiment classification on mixed text, long text can achieve to 0.9280, 0.9358 respectively, and its practical effect are more advantageous than other sentiment classification methods in terms of classification performance. The experimental results show that this study makes an important contribution to business development and has good domain extensibility.