Research on Emotional Analysis of Online Book Reviews Based on Word2Vec Method

Abstract

At present, emotional analysis in the field of natural language processing is gradually becoming a popular research direction on social media. With the rise of artificial intelligence technology and deep learning algorithms, word vector representation methods based on neural networks have emerged and emerged in the field of sentiment analysis. According to the current research results of experts, the Word2Vec model has become the mainstream word vector representation method. The algorithm model is based on continuous optimization and iteration of random initialization word vectors, ultimately obtaining a stable performance word vector representation. If the research goal is to obtain sentence vectors, weighting processing is also required. Therefore, after systematically summarizing and analyzing the representation techniques of initialized word vectors and solving sentence vectors through word vector weighting, this article proposes corresponding improvement methods. This article improves the initialization algorithm of its word vector based on the Word2Vec model. On the basis of the original randomly generated word vector, the correlation information of words is added to change the distribution characteristics of the initial word vector, thereby enhancing the performance of the word vector. In the comparative experiment of the user comment dataset, the improved model showed a 1% to 3% improvement in sentiment polarity classification accuracy compared to the other model.