Sentiment Analysis of Mid-length Microblog Based on Capsule Network

‍ ‍Aiming at the problem of obtaining user sentiment tendency through microblog text to improve the efficiency of public opinion monitoring. This paper uses deep learning to realize sentiment classification of microblog corpus， constructs a high-quality microblog sentiment classification data set that conforms to the characteristics of text length distribution in recent years， and analyzes the influence of microblog text length on sentiment classification. Due to its strong subjectivity and weak sentence relevance， the detection accuracy of the mid-length corpus is low. In response to this problem， this paper proposes a sentiment analysis model for mid-length microblog based on the capsule network. Using the attention mechanism， based on the fusion of local features and global features， the use of capsule vectors to achieve deep emotional feature extraction to improve the detection effect of mid-length corpus. Using the data set collected and constructed in this paper to conduct experiments， the results show that compared with a variety of deep learning algorithms， the performance of the model in this paper is better. In the comparative experiment on corpus of different text lengths， as the length of the text increases， the classification accuracy rate gradually decreases. Compared with the traditional LSTM algorithm， the effect of this model increases with the increase of text length， which proves the feasibility of this model for sentiment classification of mid-length microblog texts.