Subjective Text Sentiment Analysis Method Based on Deep Learning

Abstract

The invention discloses a subjective text emotion analysis method based on deep learning. The method includes the steps that 1, a C&W-SP model is established based on a C&W model, an emotion label and a word class label of a sentence are labeled in the sentence, a training set of a C&W_SPC&W-SP model is established, a C&W_SP model is trained through the training set, a word vector of each word in the training set is obtained, and a word vector file is formed; 2, a sentence vector set is established through an LSTM model according to the obtained word vector file; 3, a neutral network model is trained through the sentence vector set, and an emotion classification model is obtained; 4, the tested comment sentence is preprocessed, the tested sentence vectors are input in the emotion classification model, and the emotion tendency of the section of comment is obtained through calculation. According to the method, emotion tendency information and word class information are added into words, and the accuracy of emotion analysis is improved.

Description

technical field [0001] The invention belongs to the technical field of computer applications, and specifically relates to a method for analyzing subjective text sentiment based on deep learning. Background technique [0002] With the rapid development of the Internet, especially the gradual popularization of Web2.0 technology, the vast number of Internet users have changed from simple information acquirers in the past to major producers of Internet content. According to the "38th Statistical Report on Internet Development in China" (CNNIC, 2016) released by China Internet Network Information Center, as of June 2016, the total number of Internet users in my country has reached 710 million, and a total of 2,132 new Internet users have been added in half a year. million people, the semi-annual growth rate is 3.1%, and the Internet penetration rate is 51.7%. Such a large and fast-growing network user group coupled with the Internet application of the Web2.0 model has resulted in...

AI Technical Summary

Problems solved by technology

There are two types of building emotional dictionaries, one is manual construction, and its workload is huge, and with the development of the Internet, new emotional words emerge in an endless stream and are updated day by day, making the whole construction work very difficult; the other is through automatic However, after skipping manual work, one of the main problems of automatically constructed sentiment lexicon is its low accuracy.

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