Research on Sentiment Analysis Model of Short Text Based on Deep Learning

Abstract

With the wide application of the Internet and the rapid development of network technology, microblogs and online shopping platforms are playing an increasingly important role in people’s daily life, learning, and communication. The length of these information texts is usually relatively short, and the grammatical structure is not standardized, but it contains rich emotional tendencies of users. The features used by custumal machinery schooling methods are too sparse on the vector space model and lack the semantic information of short texts, which cannot well identify the semantic features and potential emotional features of short texts. In response to the above problems, this paper proposes a bidirectional long-term and short-term memory network model based on emotional multichannel, combining the attention mechanism and convolutional neural network features in deep learning and learning the short text by combining shallow learning and deep learning. The semantic information and potential emotional information of the short text can be improved to promote the effective expression of short-text emotional features and improve the short-text emotional classification effect. Finally, this paper compares the above models on multidomain classification data sets such as NLPIR and NLPCC2014. The accuracy and F1 value of the model proposed in this paper have achieved good improvement in the field of short-text sentiment analysis.