Research Article
User Perspective Discovery Method Based on Deep Learning in Cloud Service Community
@INPROCEEDINGS{10.1007/978-3-030-67540-0_37, author={Lei Yu and Yaoyao Wen and Shanshan Liang}, title={User Perspective Discovery Method Based on Deep Learning in Cloud Service Community}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Sentiment classification Deep learning LSTM Bi-LSTM GRU}, doi={10.1007/978-3-030-67540-0_37} }- Lei Yu
Yaoyao Wen
Shanshan Liang
Year: 2021
User Perspective Discovery Method Based on Deep Learning in Cloud Service Community
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_37
Abstract
The rapid development of cloud computing has promoted the coordinated integration of resources in various industries. In order to facilitate users’ selection and invocation, more and more individuals and organizations have moved local application resources into the cloud service communities in the form of web services. In recent years, more and more people are interested in the emotional attitudes reflected in consumer reviews, but the sentiment analysis using the deep learning method to achieve evaluation of API (Application Programming Interface) services has received little attention. In order to explore the effective information of user’s point of view data in the cloud service community, we propose an approach to analyze the user’s opinion data using deep learning. We design three deep learning models of Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). The result shows that the accuracy rate and recall rate of Bi-LSTM model is higher than the LSTM and GRU. Finally, we evaluate the performance of the three deep learning models, and choose the optimal Bi-LSTM model as the model used by the cloud service community in the future. According to the parameter comparison experiment of Bi-LSTM model, we obtained the optimal tuning of the model, and the model achieved the accuracy of 89.68%.
