Short Text Sentiment Classification Using Bayesian and Deep Neural Networks

Abstract

The previous multi-layer learning network is easy to fall into local extreme points in supervised learning. If the training samples sufficiently cover future samples, the learned multi-layer weights can be well used to predict new test samples. This paper mainly studies the research and analysis of machine short text sentiment classification based on Bayesian network and deep neural network algorithm. It first introduces Bayesian network and deep neural network algorithms, and analyzes the comments of various social software such as Twitter, Weibo, and other popular emotional communication platforms. Using modeling technology popular reviews are designed to conduct classification research on unigrams, bigrams, parts of speech, dependency labels, and triplet dependencies. The results show that the range of its classification accuracy is the smallest as 0.8116 and the largest as 0.87. These values are obtained when the input nodes of the triple dependency feature are 12,000, and the reconstruction error range of the Boltzmann machine is limited between 7.3175 and 26.5429, and the average classification accuracy is 0.8301. The advantages of triplet dependency features for text representation in text sentiment classification tasks are illustrated. It shows that Bayesian and deep neural network show good advantages in short text emotion classification.