Description

Sentiment classification of web social media faces the problem of text context semantics missing. The existing project 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. So Using Natural Language Processing to rectify the problem. 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 multi-dimension and multi-level modeling. By modeling web social media text from three dimensions (language symbols, emotions symbols and punctuation symbols) and three levels (words, sentences and documents) based on deep learning framework, to solve to find the sentiment and improve the accuracy