Deciphering Voice of the Customer using Text Analytics and Sentiment Analysis: An Interpretable Review Rating Prediction using RoBERTa
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Abstract
In this era of cut-throat competition among traditional and newer digital organizations, capturing, listening, and understanding customer voices are critical for success in the marketplace. The challenge to decipher the voice of the customer (VOC) has multiplied many times today, as now the number of customer reviews are present in multiple platforms and the data to be analyzed is huge. Sentiment analysis, and text analytics using machine learning, deep learning tools and transformer-based tools can be applied to gather meaningful insights from these data. This paper applies the traditional machine learning tools of the Naive Bayes classifier, Random Forest and AdaBoost to predict the customer review ratings. These results are compared with deep learning methods of CNN, RNN and Bi-LSTM and transformer-based approaches of BERT, DistilBERT and RoBERTa. The results show that RoBERTa has the highest accuracy among these methods. Paper also uses the explainable AI tool of LIME to understand the customer sentiments deeper in terms of why customers are giving a particular rating to the product. Business organizations will continue to use more and more AI tools to understand the customer feedback and the attempt in this paper is to learn how we can make predictions faster and more accurately.
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Aguwa, C. C., Monplaisir, L., Turgut, O. Voice of the customer: Customer satisfaction ratio based analysis. Expert 20 Systems with Applications 2012; 39(11), 10112-10119.
Jin, J., Liu, Y., Ji, P., Kwong, C. K. Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering 2019; 19(1).
Lye, S. H., Teh, P. L. Customer Intent Prediction using Sentiment Analysis Techniques. In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2021; Vol. 1, pp. 185-190. IEEE. 26
Ligthart, A., Catal, C., Tekinerdogan, B. Systematic reviews in sentiment analysis: a tertiary study. Artificial Intelligence Review 2021; 54(7), 4997-5053.
O¨ zda?go?glu, G., Kapucugil-Ikiz, A., Celik, A. F. Topic modelling-based decision framework for analysing digital voice of the customer. Total Quality Management and Business Excellence 2018; 29(13-14), 1545-1562.
Ramaswamy, S., DeClerck, N.Customer perception analysis using deep learning and NLP. Procedia Computer Science 2018; 140, 170-178.
Dhanya, N. M., Harish, U. C. Sentiment analysis of twitter data on demonetization using machine learning techniques. In Computational vision and bio inspired computing 2018; pp. 227-237. Springer, Cham.
Nandwani, P., Verma, R. A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining 2021; 11(1), 1-19.
Prottasha, N. J., Sami, A. A., Kowsher, M., Murad, S. A., Bairagi, A. K., Masud, M., Baz, M. Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning.Sensors 2022; 22(11), 4157.
Verma, S., Saini, M., Sharan, A. Deep sequential model for review rating prediction. In 2017 Tenth International Conference on Contemporary Computing (IC3),2017; pp. 1-6). IEEE.
Songpan, W. The analysis and prediction of customer review rating using opinion mining. In 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA) 2017; pp. 71- 77. IEEE.
Hadi, M. A., Fard, F. H. Evaluating pre-trained models for user feedback analysis in software engineering: A study on classification of app-reviews, 2021; arXiv:2104.05861.
Araujo, A., Golo, M., Viana, B., Sanches, F., Romero, R., Marcacini, R. From bag-of-words to pre-trained neural language models: Improving Automatic Classification of app reviews for requirements engineering. Anais Do Encontro Nacional De Inteligˆencia Artificial e Computacional (ENIAC 2020) 2020; https://doi.org/10.5753/eniac.2020.12144
Balakrishnan, V., Shi, Z., Law, C. L., Lim, R., Teh, L. L., Fan, Y. A deep learning approach in predicting products’ sentiment ratings: a comparative analysis. The Journal of Supercomputing 2022; 78(5), 7206-7226.
Kumar, K. S., Desai, J., Majumdar, J. Opinion mining and sentiment analysis on online customer review. In 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) 2016; pp. 1-4.
Grabner, D., Zanker, M., Fliedl, G., Fuchs, M. Classification of customer reviews based on sentiment analysis. In ENTER 2012; pp. 460-470.
Rajendran, S. Improving the performance of global courier and delivery services industry by analyzing the voice of customers and employees using text analytics. International Journal of Logistics Research and Applications 2021; 24(5), 473-493.
Sari, P. K., Alamsyah, A., Wibowo, S. Measuring e-Commerce service quality from online customer review using sentiment analysis. In Journal of Physics: Conference Series 2018; Vol. 971, No. 1, p. 012053. IOP Publishing.
Gallagher, C., Furey, E., Curran, K. The application of sentiment analysis and text analytics to customer experience reviews to understand what customers are really saying. International Journal of Data Warehousing and Mining (IJDWM) 2019; 15(4), 21-47.
Harikrishnan, C., Dhanya, N. M. Improving Text Classifiers Through Controlled Text Generation Using Transformer Wasserstein Autoencoder. In Inventive Communication and Computational Technologies 2022; pp. 97-105. Springer, Singapore.
Tan, K. L., Lee, C. P., Anbananthen, K. S. M., Lim, K. M. RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network. IEEE Access 2022; 10, 21517-21525.
Ribeiro, M., Singh, S., Guestrin, C. “Why should I trust you?”: Explaining the predictions of any classifier. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. 2016; https://doi.org/10.18653/v1/n16-3020
Jain, P. K., Quamer, W., Saravanan, V., Pamula, R. Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis.Journal of Ambient Intelligence and Humanized Computing 2022; pp1-13.
Mingyu, J., Jiawei, Z., Ning, W. AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model. PloS one 2022; 17(9), e0273936.
Devlin, J., Chang, M., Lee, K., Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv. 2018; https://doi.org/10.48550/arXiv.1810.04805
Liao, W., Zeng, B., Yin, X., Wei, P. An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence 2021; 51(6), 3522-3533.
Rajapaksha, P., Farahbakhsh, R., Crespi, N. BERT, XLNet or RoBERTa: The Best Transfer Learning Model to Detect Clickbaits. IEEE Access 2021; 9, 154704-154716.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettle6 moyer, L., Stoyanov, V. RoBERTa: A Robustly Optimized BERT Pretraining Approach. 2019; arXiv. https://doi.org/10.48550/arXiv.1907.11692 8
Catelli, R., Pelosi, S., Esposito, M. Lexicon-based vs. Bert-based sentiment analysis: A comparative study in Italian. Electronics 2022; 11(3), 374.
Li, W., Hu, B., Ke, S., Xiao, X., Deng, Y., Du, S. . The Application of NLP Technology In Customer Voice Analysis. In 2022 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP) 2022; pp. 894-897. IEEE.