Text emotion analysis system based on deep learning

Abstract

The invention discloses a text emotion analysis system based on deep learning. The system comprises an information collection module, an information pre-processing module, an emotion analysis module and an information display module, wherein the information collection module is used to collect comment information in each Internet resource website; the information pre-processing module is used to conduct classification, word segmentation, part-of-speech tagging, emotion information tagging processing and storage of the collected comment information; the emotion analysis module transforms the processed comment information into a phrase vector by a word representation model, a sentence module and a section and chapter model, and also inputs the phrase vector into the emotion classification model for emotion analysis; and the information display module is used to present emotion analysis results in a visualized manner. The system has the advantages that emotion orientation analysis can be conducted on the comment information; the analysis results can be presented to users in a visualized manner; and further public opinion analysis results or early warning can be provided to related departments such as enterprises or governments.

Description

technical field [0001] The invention belongs to the field of computer application technology, and specifically relates to a text sentiment analysis system 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 an unprecedente...

AI Technical Summary

Problems solved by technology

There are two types of emotional dictionary construction, one is manual construction, which has a huge workload, and with the development of the Internet, new emotional words emerge in an endless stream and are updated day by day, making the entire construction work very difficult; the other is through automatic However, after skipping the manual, one of the main problems of the automatic construction of the emotional dictionary is that the accuracy is low

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