Chinese social platform sentiment analysis method based on deep learning

Abstract

The invention discloses a Chinese social platform sentiment analysis method based on deep learning, which is characterized by comprising the following steps: 1, obtaining Chinese social platform comment original data; 2, preprocessing the text of the comment original data; 3, carrying out text word vector training; and 4, establishing a CNN-BiGRU emotion analysis model, firstly, preliminarily extracting static local features of the text by using the CNN, then, obtaining text sequence features through a bidirectional GRU, and finally, performing final emotion classification by using a Sigmoid classifier. The Chinese social platform sentiment analysis method based on deep learning can quickly and accurately analyze and extract contained sentiment tendency expressions from massive comment data.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a deep learning-based sentiment analysis method for a Chinese social platform. Background technique [0002] Sentiment Analysis (Sentiment Analysis) mainly refers to the use of natural language processing and computer linguistics to identify and extract the subjective information in the original material, and to find out the bipolar views and attitudes of the commenters on certain topics. With the continuous development and progress of information technology, the Internet has entered a period of rapid development, and people have gradually completed the transformation from information acquirers to producers. Quickly and accurately analyzing and extracting emotional tendency expressions from massive comment data has very important reference and research value for government public opinion monitoring, corporate market research and personal consumptio...

AI Technical Summary

Problems solved by technology

The method based on the emotional dictionary needs manual annotation and construction of the emotional dictionary, and the analysis results are positively correlated with the quality of the dictionary, which has great limitations; the traditional machine learning method needs to manually screen the emotional features, and the workload is huge; and the current deep learning method of sentiment analysis research , mostly devoted to the improvement of classification accuracy, often ignoring the training rate of the network

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