关键词: 词向量, CBOW模型, TF-IDF模型, 情感分析

Abstract:

In the era of big data, the emotional tendency of text is a great significance for the potential value of text mining. However, it is difficult for artificial methods to effectively exploit the potential value of comment text on the network. With the rapid development of computer technology, this problem has been effectively solved. In text sentiment analysis, acquiring emotional information of words is crucial for sentiment analysis. Word vector methods generally only model the grammatical semantics of words, but ignore the emotional information of words and cannot analyze emotions better. The weighting matrix is generated by TF-IDF algorithm model, the stop word list is constructed, and the Huffman tree is generated according to the weighting matrix as the input of the improved CBOW algorithm. The sentiment dictionary is introduced to generate the emotional label for assisting word vector generation, so that the word vector has emotional information. The experimental results show that the method can express the sentiment information well in the word vector obtained in the comment text, and the sentiment classification result is better than the traditional model. Therefore, the model can effectively improve the text sentiment classification effect in the emotional analysis of comment texts.

Key words: word vector, Continuous Bag-of-Word（CBOW） model, Term Frequency-Inverse Document Frequency（TF-IDF） model, sentiment analysis