Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (6): 21-29.doi: 10.11871/jfdc.issn.2096-742X.2020.06.003

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Sentiment Classification of Microblog Short Text Based on Feature Fusion

CHEN Tao,AN Junxiu()   

  1. Institute of Parallel Computing and Big Data, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China
  • Received:2020-07-23 Online:2020-12-20 Published:2020-12-29
  • Contact: AN Junxiu E-mail:anjunxiu@cuit.edu.cn

Abstract:

[Objective] With the rapid development of information technology and the Internet, the opinion research of microblog and other public short text is very important to study the network public opinion. Aiming at Chinese short text that represents only a small amount of information with sparse features, this paper studies the sentiment classification of microblog short text. The purpose of this paper is to improve the ability of feature extraction from microblog short text to facilitate the prediction of network public opinion. [Methods] For better motional feature extraction from microblog short text, this paper first uses the BERT model to realize the vectorization of the text, then uses CNN to extract the local semantic features of the text, and finally combines the local semantic feature vector and the feature vector trained by BERT. This method effectively solves the problem of feature extraction for Chinese short text. [Results] The experimental results show that the classification accuracy of this model is 1.24% higher than that of BiLSTM+CNN+Attenion model, 3.22% higher than BiLSTM+Attenion model, 5.24% higher than LSTM+Attenion model, 6.46% higher than Text-CNN model and 8.47% higher than SVM. [Conclusions] The proposed feature fusion model effectively improves the accuracy of text sentiment classification.

Key words: short text, sentiment analysis, neural network, feature fusion