基于深度学习的网络舆情监测系统研究

doi:10.16180/j.cnki.issn1007-7820.2022.12.014

Abstract

Abstract:

With the rapid development of the domestic Internet, network public opinion monitoring has become a part of the work of relevant departments and enterprises. Establishment of a public opinion monitoring system can detect public opinion crises in advance and deal with crisis public relations in time. The current study presents a complete framework of network public opinion monitoring system, which consists of four parts: information collection layer, data resource layer, data analysis application layer and application service layer. First, the proposed system can automatically collect data from most portals, microblogs and WeChat accounts, including articles and comments according to keywords. Then, these data are cleaned, segmented and filtered, and the word is embedded using Word2Vec model to obtain the vectorized text. The vectorized text is imported into LSTM deep learning model for sentiment analysis, and the data can be divided into sensitive data, neutral data and non-sensitive data. Finally, the public opinion warning information is displayed by visualization technology. The proposed network public opinion monitoring system can help regulators to monitor and guide relevant public opinions in a timely manner, and promote the harmonious development of society.

Key words: network public opinion, public opinion monitoring, sentiment analysis, data analysis, deep learning, topic detection and tracking, convolutional neural network, long short-term memory

CLC Number:

TP391.1

DENG Lei,SUN Peiyang. Research on Network Public Opinion Monitoring System Based on Deep Learning[J].Electronic Science and Technology, 2022, 35(12): 97-102.

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Research on Network Public Opinion Monitoring System Based on Deep Learning

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