Research on Sentiment Analysis of Micro-blog based on Attention-BiLSTM

The rapid development of the Internet gives birth to massive data. These data have the characteristics of multiple types, low density, and fast transmission speed. As one of the products of the era of big data, micro-blog provides users with a highly interactive platform. In order to explore the value and sentiment contained in micro-blog comment texts, and solve the problem of semantic loss and over-reliance on manual work in traditional text sentiment analysis, this paper proposes a deep learning model based on Attention-BiLSTM. In the embedding layer, the CBOW method of Word2vec is used to transform the text data into word vectors, and the attention mechanism is integrated to dynamically weight the input word vectors. Secondly, BiLSTM is used to extract text features, then fuse them in concat layer. After that the classification is realized by Softmax whose results of the output text are positive or negative. Finally, by comparing with TextCNN, LSTM and BiLSTM, the result shows that the Attention-BiLSTM model does have better classification effect, strong generalization and practicability.