Sentiment Analysis of Airline Reviews:Based on BERT-CNN Model

Pei Gan

doi:10.47297/taposatWSP2633-456931.20220303

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Sentiment Analysis of Airline Reviews:Based on BERT-CNN Model

Volume 3, Issue 3

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Sentiment Analysis of Airline Reviews:Based on BERT-CNN Model
Sentiment Analysis of Airline Reviews:Based on BERT-CNN Model
School of Computer Science & Engineering, UESTC,Sichuan,Chengdu,P.R. China,611731
Vol. 3, Issue 3, Pages: 153-158(2022)
Published： 2022 ，
DOI：10.47297/taposatWSP2633-456931.20220303
Accepted：

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PEI GAN. (2022). Sentiment Analysis of Airline Reviews:Based on BERT-CNN Model. Theory and Practice of Science and Technology, 3(3), 153-158..
DOI：

PEI GAN. (2022). Sentiment Analysis of Airline Reviews:Based on BERT-CNN Model. Theory and Practice of Science and Technology, 3(3), 153-158.. DOI： 10.47297/taposatWSP2633-456931.20220303.

摘要

Abstract

Passengers' reviews of airline service are very crucial for airline companies to retain passengers and improve the quality of their service. To improve the accuracy of sentiment analysis for reviews of airline services

this paper proposed a network model based on BERT and CNN. In this paper

we processed a dataset provided by CAPSE containing five major Chinese airlines. The BERTCNN model is used to extract sentiment information from Chinese passengers' reviews of airline service and finally trained on the dataset to compare the classification accuracy of BERT

CNN

and Na?ve Bayes models. We trained our airline review classifier by dividing the training data in a ratio of 9:1. We evaluate the performance of each model by calculating the accuracy of the classification. Experimental results show that the accuracy of classification using the BERT-CNN model is better than using BERT and CNN model alone. BERT-CNN allows for more accurate sentiment analysis of Chinese passengers' reviews of airline service.

关键词

Keywords

Artificial intelligenceMachine learningBERTSentiment analysisText classification

references

Ravi, K., Ravi, V. (2015) A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89: 14-46.[2] Medhat, W., Hassan, A., Korashy, H. (2014) Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5: 1093-1113.[3] Kim, Y. (2014) Convolutional Neural Networks for Sentence Classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha. pp. 1746-51.[4] Devlin, J., Chang, M.W., Lee, K., et al. (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis. pp. 4171-86.[5] Lee, C.C., Gao, Z., Tsai, C.L. (2020) BERT-Based Stock Market Sentiment Analysis. In: 2020 IEEE International Conference on Consumer Electronics-Taiwan. Taiwan. pp. 1-2.[6] Rane, A., Kumar, A. (2018) Sentiment Classification System of Twitter Data for US Airline Service Analysis. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference. Tokyo. pp. 769-73.[7] Wan, Y., Gao, Q. (2015) An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis. In: 2015 IEEE International Conference on Data Mining Workshop. Atlantic. pp. 1318-25.

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