Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction

References

• Ain, Q. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., & Rehman, A. (2017). Sentiment analysis using deep learning techniques: A review. International Journal of Advanced Computer Science and Applications, 8(6), 424–433. https://doi.org/https://doi.org/10.14569/IJACSA.2017.080657
   Web of Science ®Google Scholar
• Araque, O., Corcuera-Platas, I., Sánchez-Rada, J. F., & Iglesias, C. A. (2017). Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications, 77, 236–246. https://doi.org/https://doi.org/10.1016/j.eswa.2017.02.002
   Web of Science ®Google Scholar
• Bhattacharyya, S. (2013). Confidence in predictions from random tree ensembles. Knowledge and Information Systems, 35(2), 391–410. https://doi.org/https://doi.org/10.1007/s10115-012-0600-z
   Web of Science ®Google Scholar
• Ding, R.-X., Palomares, I., Wang, X., Yang, G.-R., Liu, B., Dong, Y., Herrera-Viedma, E., & Herrera, F. (2020). Large-scale decision-making: Characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective. Information Fusion, 59, 84–102. https://doi.org/https://doi.org/10.1016/j.inffus.2020.01.006
   Web of Science ®Google Scholar
• Dinga, R., Penninx, B. W. J. H., Veltman, D. J., Schmaal, L., & Marquand, A. F. (2019). Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines. BioRxiv, https://doi.org/https://doi.org/10.1101/743138
   Google Scholar
• Floh, A., Koller, M., & Zauner, A. (2013). Taking a deeper look at online reviews: The asymmetric effect of valence intensity on shopping behaviour. Journal of Marketing Management, 29(5–6), 646–670. https://doi.org/https://doi.org/10.1080/0267257X.2013.776620
   Google Scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.
   Google Scholar
• Haenlein, M., & Kaplan, A. M. (2010). An empirical analysis of attitudinal and behavioral reactions toward the abandonment of unprofitable customer relationships. Journal of Relationship Marketing, 9(4), 200–228. https://doi.org/https://doi.org/10.1080/15332667.2010.522474
   Google Scholar
• Hossain, R., Ahamed, F., Zannat, R., & Rabbani, M. G. (2013). Comparative sentiment analysis using difference types of machine learning algorithm. 8th International Conference System Modeling and Advancement in Research Trends (SMART). (pp. 329–333).
   Google Scholar
• Ismagilova, E., Dwivedi, Y. K., Slade, E., & Williams, M. D. (2017). Electronic Word of Mouth (eWOM) in the marketing context. A state of the Art analysis and future directions. Springer.
   Google Scholar
• Kim, J., & Gupta, P. (2012). Emotional expressions in online user reviews: How they influence consumers’ product evaluations. Journal of Business Research, 65(7), 985–992. https://doi.org/https://doi.org/10.1016/j.jbusres.2011.04.013
   Web of Science ®Google Scholar
• Liu, B. (2010). Sentiment analysis and subjectivity. In: Handbook of natural language processing. (2nd ed). Taylor and Francis Group.
   Google Scholar
• Luo, F., Li, C., & Cao, Z. (2016). Affective-feature-based sentiment analysis using SVM classifier. IEEE 20th International Conference on Computer Supported Cooperative Work in Design (pp. 276–281.
   Google Scholar
• Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational (pp. 142–150).
   Google Scholar
• Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis – a review of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32. https://doi.org/https://doi.org/10.1016/j.cosrev.2017.10.002
   Web of Science ®Google Scholar
• Messoudi, S., Rousseau, S., & Destercke, S. (2020). Deep conformal prediction for robust models. In M. J. Lesot (Ed.), Information processing and Management of uncertainty in knowledge-based systems. IPMU 2020. Communications in Computer and information science (pp. 528–540). Springer.
   Google Scholar
• Norinder, U., Myatt, G., & Ahlberg, E. (2018). Predicting aromatic amine mutagenicity with confidence: A case study using conformal prediction. Biomolecules, 8(3), 85. https://doi.org/https://doi.org/10.3390/biom8030085
   Web of Science ®Google Scholar
• Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/https://doi.org/10.1561/1500000011
   Google Scholar
• Redhu, S., Srivastava, S., Bansal, B., & Gupta, G. (2018). Sentiment analysis using text mining: A review. International Journal on Data Science and Technology, 4(2), 49–53. https://doi.org/https://doi.org/10.11648/j.ijdst.20180402.12
   Google Scholar
• Shrestha, N., & Nasoz, F. (2019). Deep learning sentiment analysis of Amazon.com reviews and ratings. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 8(1), 1–15. https://doi.org/https://doi.org/10.5121/ijscai.2019.8101
   Google Scholar
• Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic learning in a random world. Springer. pp. 1–324.
   Google Scholar
• Wang, X., Zhao, Y., & Pourpanah, F. (2020). Recent advances in deep learning. International Journal of Machine Learning and Cybernetics, 11(4), 747–750. https://doi.org/https://doi.org/10.1007/s13042-020-01096-5
   Web of Science ®Google Scholar
• Yaakub, M. R., Latiffi, M. I. A., & Zaabar, L. S. (2019). A review on sentiment analysis techniques and applications, 4–5 February 2019 IOP Conference Series: Materials Science and Engineering 551 012070.
   Google Scholar
• Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. WIRES Data Mining and Knowledge Discovery, 8(4), e1253. https://doi.org/https://doi.org/10.1002/widm.1253
   Web of Science ®Google Scholar