Sentiment Analysis of Weak-RuleText Based on the Combination of Sentiment Lexicon and Neural Network

摘要

Aiming at social media texts with weak grammatical features, based on the weak-rule text and word vectors, this paper proposes a framework combining sentiment lexicon and neural network. Moreover, we study the influence of sentiment lexicon on improving the accuracy of text sentiment analysis. According to the structure of the word vector and the uniqueness of the sentiment score in the sentiment lexicon, our model first converts the text into a sentiment score vector. Secondly, the converted sentiment score vector is used to learn its hidden emotional representation through the neural network. At the same time, we use a combination of pre-training word vectors and the BiLSTM network to learn the hidden semantic representation of the original text. Finally, the hidden emotional representation and the hidden semantic representation are combined to analyze the weak-rule text sentiment. Experimental results on Weibo text with weak grammatical rules show that the performance of the method which use the sentiment score vector is better than traditional sentiment lexicon methods. The performance of combining hidden emotion representation and hidden semantic representation is also better than the BiLSTM that uses pre-trained word vectors and attention mechanism.