Sentiment Analysis About Investors and Consumers in Energy Market Based on BERT-BiLSTM

With the rapid development of social media, the number of online comments has exploded, and more and more people are willing to express their attitudes and feelings on the Internet. Under the influence of a series of major events all over the world, production order is facing a serious challenge, wh...

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Bibliographic Details
Published in:	IEEE access 2020, Vol.8, p.171408-171415
Main Authors:	Cai, Ren, Qin, Bin, Chen, Yangken, Zhang, Liang, Yang, Ruijiang, Chen, Shiwei, Wang, Wei
Format:	Article
Language:	eng
Subjects:	BERT-BiLSTM Bit error rate Coders combination model Consumers Data mining Digital media Energy industry energy market Internet investors and consumers Machine learning Mathematical models Orientation Predictive models Representations Semantics Sentiment analysis Short term Statistical methods Task analysis Technical services Training Transformers
Online Access:	Get full text
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Summary:	With the rapid development of social media, the number of online comments has exploded, and more and more people are willing to express their attitudes and feelings on the Internet. Under the influence of a series of major events all over the world, production order is facing a serious challenge, which severely impact on energy market. In 2020, a large number of investors' and consumers' comments related to social events began to appear on the Internet from China. However, the style and quality of online comments vary greatly, making it difficult to accurately extract users' views and tendencies. Based on the investors' and consumers' statements published on Chinese Internet, in this paper, we use statistical methods to classify the sentiment orientation of the Netizens firstly, and then use Bidirectional Encoder Representations from Transformer-Bidirectional Long Short Term Memory (BERT-BiLSTM) which is the combination forecasting method of Bidirectional Encoder Representations from Transformer (BERT) and Bidirectional Long Short Term Memory (BiLSTM), to model and forecast the sentiment orientation of users' statements, as well as being compared with its based models, BERT and BiLSTM. Among them, the accuracy and recall, which represent the predictive abilities on the overall samples and on the focus(the samples of Label 1) respectively, of BERT-BiLSTM model are 0.8620 and 0.7078 respectively, which are superior to those of BERT model (0.8559 and 0.5576) and LSTM model (0.7775 and 0.0747). The research results can accurately predict the sentiment orientation of Internet users during the social events so as to provide technical support for grasping the energy market trend.
ISSN:	2169-3536 2169-3536