A Sentiment Classification Method of Web Social Media Based on Multidimensional and Multilevel Modeling

Abstract

Sentiment classification of web social media faces the problem of text context semantics missing. The existing research mainly solves the problem of text context semantic missing by mining language symbol information in web social media text, seldom considering the emoticon symbols and punctuation symbols in web social media text. Similar to language symbols, emoticons’ symbols and punctuation symbols in web social media text also contain certain sentiment information. In order to make full use of sentiment information contained in web social media to solve the problem of text context semantics missing, we propose a sentiment classification method of web social media based on multidimensional and multilevel modeling. By modeling web social media text from three dimensions (language symbols, emoticons’ symbols, and punctuation symbols) and three levels (words, sentences, and documents) based on a deep learning framework, in this article, we attempt to solve text context semantics missing faced by the sentiment classification of web social media and improve the accuracy of sentiment classification of web social media. The experimental results on Sina Weibo and Twitter datasets show that the average accuracy of our method is 0.9479, which achieves more than 5.86% performance compared with the existing sentiment classification methods.