Mining Online Reviews with Fake Review Detection to Improve Logistics Service Quality
DOI:
https://doi.org/10.52731/lbds.v005.433Keywords:
Online review analytics, fake review detection, topic modeling, logistics service qualityAbstract
This study proposes an analytical framework to identify important information in online reviews and uses the service quality models and relevant literature on logistics service quality to help construct and analyze the logistics service quality topics. First, this study establishes a fake review detection model based on the convolution neural network (CNN) model to remove potential fake reviews. Then, the SERVQUAL and E-S-QUAL models are used to assist in constructing a Latent Dirichlet Allocation (LDA) topic model to identify relevant topics mentioned in the online reviews about logistics service quality. In further data analysis, this study segments the comments, calculates the sentiment scores of the sentences that mention the logistics service quality in the online reviews, and uses the number of helpfulness votes of each online review to calculate the weight of the sentiment score.
References
Chatterjee, S., Goyal, D., Prakash, A. and Sharma, J. (2021). Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application. Journal of Business Research, 131, 815-825, doi: 10.1016/j.jbusres.2020.10.043.
Lim, M. K., Li, Y. and Song, X. (2021). Exploring customer satisfaction in cold chain logistics using a text mining approach. Industrial Management & Data Sys-tems, 121(12), 2426-2449, doi: 10.1108/IMDS-05-2021-0283.
Leem, B. H. and Eum, S. W. (2021). Using text mining to measure mobile banking service quality. Industrial Management & Data Systems, 121(5), 993–1007, doi: 10.1108/IMDS-09-2020-0545.
Zhao, J., Gui. X., and Zhang. X. (2018). Deep Convolution Neural Networks for Twitter Sentiment Analysis. IEEE Access, 6, 23253-23260, doi: 10.1109/ACCESS.2017.2776930.
Hajek, P., Barushka, A. and Munk, M. (2020). Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Computing and Applications, 32, 17259–17274, doi: 10.1007/s00521-020-04757-2.
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R. and Mora, H. (2020). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. In-dustrial Marketing Management, 90, 523-537, doi: 10.1016/j.indmarman.2019.08.003.
Paul, H. and Nikolaev, A. (2021). Fake review detection on online E-commerce platforms: a systematic literature review. Data Mining and Knowledge Discovery, 25, 1830–1881, doi: 10.1007/s10618-021-00772-6.
Lee, P. J., Hu, Y. H. and Lu, K. T. (2018). Assessing the helpfulness of online hotel reviews: A classification-based approach. Telematics and Informatics, 35(2), 436–445, doi: 10.1016/j.tele.2018.01.001.
Cao, Q., Duan, W. and Gan, Q. (2011). Exploring determinants of voting for the ‘helpfulness’ of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521, doi: 10.1016/j.dss.2010.11.009.
Yang, S., Zhou, Y., Yao, J., Chen, Y. and Wei, J. (2019). Understanding online review helpfulness in omnichannel retailing. Industrial Management & Data Sys-tems, 119(8), 1565–1580, doi: 10.1108/IMDS-10-2018-0450.
Wu, P. J. and Lin, K. C. (2018). Unstructured big data analytics for retrieving e-commerce logistics knowledge. Telematics and Informatics, 35(1), 237–244, doi: 10.1016/j.tele.2017.11.004.
Wu, P. J. and Yang, C. K. (2018). Unstructured big data analytics for air cargo logistics management. 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Singapore, 2018, 274-278, doi: 10.1109/SOLI.2018.8476741.
Agrawal, R., Imielinski, T. and Swami, A. (1993) Mining Association Rules be-tween Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington DC, May 1993, 207-216, doi: 10.1145/170036.170072.
Salminen, J., Kandpal, C., Kamel, A. M., Jung, S. and Jansen, B. J. (2022). Cre-ating and detecting fake reviews of online products. Journal of Retailing and Con-sumer Services, 64, 102771, doi: 10.1016/j.jretconser.2021.102771.
Ahmed, H., Traore, I. and Saad, S. (2018). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1), e9, doi: doi.org/10.1002/spy2.9.
Ott, M., Choi, Y., Cardie, C. and Hancock, J. T. (2011). Finding Deceptive Opinion Spam by Any Stretch of the Imagination. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 309–319, Portland, Oregon, USA. Association for Computational Linguistics, https://arxiv.org/abs/1107.4557.
Parasuraman, A. Zeithaml, V. A. and Berry, L. L. (1988). SERVQUAL: A Multi-ple-Item Scale for Measuring Consumer Perceptions of Service Quality, Journal of Retailing, 64(1), 12–40.
Parasuraman, A., Zeithaml, V. A. and Malhotra, A. (2005). E-S-QUAL: A Multi-ple-Item Scale for Assessing Electronic Service Quality. Journal of Service Research, 7(3), 213–233, doi: 10.1177/1094670504271156.
Chakrabarti, S., Trehan, D. and Makhija, M. (2018). Assessment of service quality using text mining – evidence from private sector banks in India. International Journal of Bank Marketing, 36(4), 594-615, doi: 10.1108/IJBM-04-2017-0070.
Hong, W., Zheng, C., Wu, L. and Pu, X. (2019). Analyzing the Relationship be-tween Consumer Satisfaction and Fresh E-Commerce Logistics Service Using Text Mining Techniques. Sustainability, 11(13), 3570, doi: 10.3390/su11133570.
Nielsen, F. Å. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. [online] Available at: https://arxiv.org/abs/1103.2903 [Ac-cessed 19 May 2022].
Ni, J., Li, J. and McAuley, J. (2019). Justifying Recommendations using Distant-ly-Labeled Reviews and Fine-Grained Aspects. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 188–197, Hong Kong, China. Association for Computational Linguistics, doi: 10.18653/v1/D19-1018.
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022, doi: 10.5555/944919.944937.
