Natural Language Processing for Customer Service Chatbots: Enhancing Customer Experience
Keywords:
: chatbots; natural language processing; customer service; intent classification; named entity recognition; sentiment analysis; dialogue management; user experienceAbstract
This paper explores the application of natural language processing (NLP) techniques to improve the performance and user experience of customer service chatbots. Chatbots are increasingly being deployed by companies to provide 24/7 customer support and handle common queries. However, many chatbots still struggle to engage in natural conversations and accurately understand and address customer needs. We propose leveraging advanced NLP methods including intent classification, named entity recognition, sentiment analysis, and dialogue management to enable chatbots to better comprehend user messages and provide more helpful and personalized responses. We present a modular framework for building NLP-powered chatbots and demonstrate its effectiveness through experiments across diverse customer service domains. Results show that our NLP techniques significantly enhance chatbot performance on key metrics such as intent understanding accuracy, user query resolution rate, conversation quality, and overall customer satisfaction compared to traditional rule-based and retrieval-based chatbots. Our work illustrates the immense potential of NLP to empower intelligent, scalable, and user-friendly chatbot systems that can strengthen customer relationships and support.
Downloads
References
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.
Williams, J. D., Niraula, N. B., Dasigi, P., Lakshmiratan, A., Suarez, C. G. J., Reddy, M., & Zweig, G. (2015). Rapidly scaling dialog systems with interactive learning. In Natural Language Dialog Systems and Intelligent Assistants (pp. 1-13). Springer, Cham.
Vinyals, O., & Le, Q. (2015). A neural conversational model. arXiv preprint arXiv:1506.05869.
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016, February). Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. In AAAI (Vol. 16, pp. 3776-3784).
Liu, B., & Lane, I. (2016, December). Attention-based recurrent neural network models for joint intent detection and slot filling. In Interspeech (pp. 685-689).
Hakkani-Tür, D., Tür, G., Celikyilmaz, A., Chen, Y. N., Gao, J., Deng, L., & Wang, Y. Y. (2016, June). Multi-domain joint semantic frame parsing using bi-directional rnn-lstm. In Interspeech (pp. 715-719).
Gupta, R., Rastogi, A., & Hakkani-Tur, D. (2018). An efficient approach to encoding context for spoken language understanding. arXiv preprint arXiv:1807.00267.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016, November). Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing (pp. 606-615).
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014, August). SemEval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014) (pp. 27-35).
Goddeau, D., Meng, H., Polifroni, J., Seneff, S., & Busayapongchai, S. (1996). A form-based dialogue manager for spoken language applications. In Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96 (Vol. 2, pp. 701-704). IEEE.
Sukhbaatar, S., Weston, J., & Fergus, R. (2015). End-to-end memory networks. In Advances in neural information processing systems (pp. 2440-2448).
Li, X., Chen, Y. N., Li, L., Gao, J., & Celikyilmaz, A. (2017). End-to-end task-completion neural dialogue systems. arXiv preprint arXiv:1703.01008.
Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., & Zhou, M. (2017, July). Superagent: A customer service chatbot for e-commerce websites. In Proceedings of ACL 2017, System Demonstrations (pp. 97-102).
Xu, Z., Liu, B., Wang, B., Sun, C., & Wang, X. (2017, February). Incorporating loose-structured knowledge into conversation modeling via recall-gate LSTM. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 3506-3513). IEEE.
Huang, M., Zhu, X., & Gao, J. (2020). Challenges in building intelligent open-domain dialog systems. ACM Transactions on Information Systems (TOIS), 38(3), 1-32.
Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991.
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002, July). BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311-318).
Raphael, I., Mahesula, S., Kalsaria, K., Kotagiri, V., Purkar, A. B., Anjanappa, M., & ... (2012). Microwave and magnetic (M2) proteomics of the experimental autoimmune encephalomyelitis animal model of multiple sclerosis. Electrophoresis, 33(24), 3810-3819.
Salzler, R. R., Shah, D., Doré, A., Bauerlein, R., Miloscio, L., Latres, E., & ... (2016). Myostatin deficiency but not anti‐myostatin blockade induces marked proteomic changes in mouse skeletal muscle. Proteomics, 16(14), 2019-2027.
Shah, D., Anjanappa, M., Kumara, B. S., & Indiresh, K. M. (2012). Effect of post-harvest treatments and packaging on shelf life of cherry tomato cv. Marilee Cherry Red. Mysore Journal of Agricultural Sciences.
Shah, D., Dhanik, A., Cygan, K., Olsen, O., Olson, W., & Salzler, R. (2020). Proteogenomics and de novo sequencing based approach for neoantigen discovery from the immunopeptidomes of patient CRC liver metastases using Mass Spectrometry. The Journal of Immunology, 204(1_Supplement), 217.16-217.16.
Shah, D., Salzler, R., Chen, L., Olsen, O., & Olson, W. (2019). High-Throughput Discovery of Tumor-Specific HLA-Presented Peptides with Post-Translational Modifications. MSACL 2019 US.
Srivastava, M., Copin, R., Choy, A., Zhou, A., Olsen, O., Wolf, S., Shah, D., & ... (2022). Proteogenomic identification of Hepatitis B virus (HBV) genotype-specific HLA-I restricted peptides from HBV-positive patient liver tissues. Frontiers in Immunology, 13, 1032716.
Tripathi, A. (2023). Low-code/no-code development platforms. International Journal of Computer Applications (IJCA), 4(1), 27-35. https://iaeme.com/Home/issue/IJCA?Volume=4&Issue=1. ISSN Online: 2341-7801
Tripathi, A. (2022). Optimal serverless deployment methodologies: Ensuring smooth transitions and enhanced reliability. Journal of Computer Engineering and Technology (JCET), 5(1), 21-28. https://iaeme.com/Home/issue/JCET?Volume=5&Issue=1. ISSN Print: 2347-3908. ISSN Online: 2347-3916.
Patil, Sanjaykumar Jagannath et al. "AI-Enabled Customer Relationship Management: Personalization, Segmentation, and Customer Retention Strategies." International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. 12, no. 21s, 2024, pp. 1015–1026.
https://ijisae.org/index.php/IJISAE/article/view/5500
Dodda, Suresh, Suman Narne, Sathishkumar Chintala, Satyanarayan Kanungo, Tolu Adedoja, and Dr. Sourabh Sharma. "Exploring AI-driven Innovations in Image Communication Systems for Enhanced Medical Imaging Applications." J.ElectricalSystems 20, no. 3 (2024): 949-959.
https://journal.esrgroups.org/jes/article/view/1409/1125
https://doi.org/10.52783/jes.1409
Predictive Maintenance and Resource Optimization in Inventory Identification Tool Using ML. (2020). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 8(2), 43-50. https://ijope.com/index.php/home/article/view/127
Pradeep Kumar Chenchala. (2023). Social Media Sentiment Analysis for Enhancing Demand Forecasting Models Using Machine Learning Models. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6), 595–601. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10762
Varun Nakra. (2024). AI-Driven Predictive Analytics for Business Forecasting and Decision Making. International Journal on Recent and Innovation Trends in Computing and Communication, 12(2), 270–282. Retrieved from
Savitha Naguri, Rahul Saoji, Bhanu Devaguptapu, Pandi Kirupa Gopalakrishna Pandian,Dr. Sourabh Sharma. (2024). Leveraging AI, ML, and Data Analytics to Evaluate Compliance Obligations in Annual Reports for Pharmaceutical Companies. Edu Journal of International Affairs and Research, ISSN: 2583-9993, 3(1), 34–41. Retrieved from https://edupublications.com/index.php/ejiar/article/view/74
Dodda, Suresh, Navin Kamuni, Venkata Sai Mahesh Vuppalapati, Jyothi Swaroop Arlagadda Narasimharaju, and Preetham Vemasani. "AI-driven Personalized Recommendations: Algorithms and Evaluation." Propulsion Tech Journal 44, no. 6 (December 1, 2023). https://propulsiontechjournal.com/index.php/journal/article/view/5587.
Kamuni, Navin, Suresh Dodda, Venkata Sai Mahesh Vuppalapati, Jyothi Swaroop Arlagadda, and Preetham Vemasani. "Advancements in Reinforcement Learning Techniques for Robotics." Journal of Basic Science and Engineering 19, no. 1 (2022): 101-111. ISSN: 1005-0930.
Dodda, Suresh, Navin Kamuni, Jyothi Swaroop Arlagadda, Venkata Sai Mahesh Vuppalapati, and Preetham Vemasani. "A Survey of Deep Learning Approaches for Natural Language Processing Tasks." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 12 (December 2021): 27-36. ISSN: 2321-8169. http://www.ijritcc.org.
Jigar Shah , Joel lopes , Nitin Prasad , Narendra Narukulla , Venudhar Rao Hajari , Lohith Paripati. (2023). Optimizing Resource Allocation And Scalability In Cloud-Based Machine Learning Models. Migration Letters, 20(S12), 1823–1832. Retrieved from https://migrationletters.com/index.php/ml/article/view/10652
Joel lopes, Arth Dave, Hemanth Swamy, Varun Nakra, & Akshay Agarwal. (2023). Machine Learning Techniques And Predictive Modeling For Retail Inventory Management Systems. Educational Administration: Theory and Practice, 29(4), 698–706. https://doi.org/10.53555/kuey.v29i4.5645
Narukulla, Narendra, Joel Lopes, Venudhar Rao Hajari, Nitin Prasad, and Hemanth Swamy. "Real-Time Data Processing and Predictive Analytics Using Cloud-Based Machine Learning." Tuijin Jishu/Journal of Propulsion Technology 42, no. 4 (2021): 91-102.
Nitin Prasad. (2022). Security Challenges and Solutions in Cloud-Based Artificial Intelligence and Machine Learning Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 286–292. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10750
Varun Nakra, Arth Dave, Savitha Nuguri, Pradeep Kumar Chenchala, Akshay Agarwal. (2023). Robo-Advisors in Wealth Management: Exploring the Role of AI and ML in Financial Planning. European Economic Letters (EEL), 13(5), 2028–2039. Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/1514
Varun Nakra. (2023). Enhancing Software Project Management and Task Allocation with AI and Machine Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 1171–1178. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10684
Shah, Darshit, Ankur Dhanik, Kamil Cygan, Olav Olsen, William Olson, and Robert Salzler. "Proteogenomics and de novo Sequencing Based Approach for Neoantigen Discovery from the Immunopeptidomes of Patient CRC Liver Metastases Using Mass Spectrometry." The Journal of Immunology 204, no. 1_Supplement (2020): 217.16-217.16. American Association of Immunologists.
Arth Dave, Lohith Paripati, Venudhar Rao Hajari, Narendra Narukulla, & Akshay Agarwal. (2024). Future Trends: The Impact of AI and ML on Regulatory Compliance Training Programs. Universal Research Reports, 11(2), 93–101. Retrieved from https://urr.shodhsagar.com/index.php/j/article/view/1257
Arth Dave, Lohith Paripati, Narendra Narukulla, Venudhar Rao Hajari, & Akshay Agarwal. (2024). Cloud-Based Regulatory Intelligence Dashboards: Empowering Decision-Makers with Actionable Insights. Innovative Research Thoughts, 10(2), 43–50. Retrieved from https://irt.shodhsagar.com/index.php/j/article/view/1272
Cygan, K. J., Khaledian, E., Blumenberg, L., Salzler, R. R., Shah, D., Olson, W., & ... (2021). Rigorous estimation of post-translational proteasomal splicing in the immunopeptidome. bioRxiv, 2021.05.26.445792.
Mahesula, S., Raphael, I., Raghunathan, R., Kalsaria, K., Kotagiri, V., Purkar, A. B., & ... (2012). Immunoenrichment microwave and magnetic proteomics for quantifying CD 47 in the experimental autoimmune encephalomyelitis model of multiple sclerosis. Electrophoresis, 33(24), 3820-3829.
Mahesula, S., Raphael, I., Raghunathan, R., Kalsaria, K., Kotagiri, V., Purkar, A. B., & ... (2012). Immunoenrichment Microwave & Magnetic (IM2) Proteomics for Quantifying CD47 in the EAE Model of Multiple Sclerosis. Electrophoresis, 33(24), 3820.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
