Open AccessComment information extraction based on LSTM and Neural Networks
Author(s) -
Qingliang Zhang,
Binning Ma,
Xier Zhong,
Mei Liang-cai,
Youyu Zhou
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2031/1/012037
Subject(s) - computer science , sentiment analysis , artificial neural network , classifier (uml) , focus (optics) , artificial intelligence , word (group theory) , product (mathematics) , cloud computing , machine learning , natural language processing , data science , linguistics , philosophy , physics , geometry , mathematics , optics , operating system
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.