A CNN-BiLSTM algorithm for Weibo emotion classification with attention mechanism

Abstract

Weibo short text information contains a large amount of network language, emoticons, etc., and due to the long-time span of the content, the emotions of the posts posted by people often change due to time or the occurrence of certain special events. Therefore, traditional sentiment analysis methods are not suitable for this task. This article proposes a CNNs-Bi LSTM sentiment analysis method that integrates attention mechanism. It combines convolutional neural networks and bidirectional short-term memory networks to obtain keyword information in text through attention mechanism, efficiently and accurately realizing data temporal and semantic information mining. Through experimental verification using Weibo public opinion data, the results show that this method achieves higher accuracy compared to other benchmark models and can fully utilize multidimensional matrices to capture rich text features, with certain advantages.