Comment information extraction based on LSTM and Neural Networks

With the advent of the era of big data, the amount of data has also increased geometrically. People’s ability to obtain effective information has gradually declined. At present, most e-commerce platforms only focus on the sentiment analysis of positive and negative reviews. It is difficult for users...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-09, Vol.2031 (1), p.12037
Main Authors: Zhang, Qingliang, Ma, Binning, Zhong, Xier, Mei, Liangcai, Zhou, Youyu
Format: Article
Language:English
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Summary:With the advent of the era of big data, the amount of data has also increased geometrically. People’s ability to obtain effective information has gradually declined. At present, most e-commerce platforms only focus on the sentiment analysis of positive and negative reviews. It is difficult for users and businesses to extract user opinions and views from the massive review data. For the product review data of a certain hard disk, use the LSTM model to train the sentiment classification model. Finally, the neural network is used to find the keywords of the comment data and the word cloud diagram is used to display the analysis results. Through the research, it can be found that LSTM emotion classifier can classify comments with high accuracy and words closely related to comment emotion tendency can be found according to the weight of neural network.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2031/1/012037