Hybrid Neural Network for Sina Weibo Sentiment Analysis

Abstract

Sina Weibo sentiment analysis technology provides the methods to survey public emotion about the related events or products in China. Most of the current works in sentiment analysis are to apply neural networks, such as convolution neural network (CNN), long short-term memory (LSTM), or C-LSTM. In this article, a novel structure of a hybrid neural network model is proposed to deal with the polysemy phenomena of words and topic confusion with Sina Weibo. First, the embeddings from language models (ELMo) and some statistical methods based on the corpus and sentiment lexicon are employed to extract the features. This method uses latent semantic relationships in different linguistic contexts and cooccurrence statistical features between words in Weibo. Second, for the classification model, unlike traditional C-LSTM which feeds CNN’s output into LSTM, we employ several filters with variable window sizes to extract a sequence of high-level word representation in different granularity distributions of text data in multichannel CNN. At the same time, obtain the sentence representation in Bi-LSTM. Then, concatenate the outputs of multichannel CNN and Bi-LSTM. In conclusion, the results indicate that the proposed model performs better on the precision, recall, and F1-score for Weibo sentiment analysis.