Sentiment Analysis and Deep Learning Based Chatbot for User Feedback

  • NivethanEmail author
  • Sriram Sankar
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)


Recently, the conversational agents like Chatbots are widely employed for achieving a better Human-Computer Interaction (HCI). In this paper, a retrieval based chatbot is designed using Natural Language Processing (NLP) techniques and a Multilayer Perceptron (MLP) neural network. The purpose of the chatbot is to extract user’s feedback based on the services provided to them. User feedback is a very essential component for the betterment of the service. Chatbot serves as a better interface for obtaining an appropriate user feedback. Furthermore, sentiment analysis is done on the feedback as a result a suitable response is delivered to the user. A Long Short Term Neural Network (LSTM) is used to classify the sentiment of the feedback.


Chatbot Sentiment analysis User feedback Deep learning 


  1. 1.
    Basari, A.S.H., Hussin, B., Ananta, I.G.P., Zeniarja, J.: Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimisation. Procedia Eng. 53, 453–462 (2013)CrossRefGoogle Scholar
  2. 2.
    Behera, B.: bibek@magictiger.comGoogle Scholar
  3. 3.
    Bravo-Marquez, F., Mendoza, M., Poblete, B.: Combining strengths, emotions and polarities for boosting twitter sentiment analysis. In: Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, p. 2 (2013)Google Scholar
  4. 4.
    Kaggle Amazon Reviews for Sentiment Analysis.
  5. 5.
    Khan, A.Z., Atique, M., Thakare, V.: Combining lexicon-based and learning based methods for twitter sentiment analysis. Int. J. Electron. Commun. Soft Comput. Sci. Eng. (IJECSCSE), 89 (2015)Google Scholar
  6. 6.
    Lowe, R., Pow, N., Serban, I., Pineau, J.: The ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems (2015). arXiv:1506.08909
  7. 7.
    Pandey, A.C., Rajpoot, D.S., Muskesh, S.: Twitter sentiment analysis using hybrid cuckoo search method (2017).
  8. 8.
    Wang, H., Lu, Z., Li, H., Chen, E.: A dataset for research on short-text conversations. In: EMNLP, pp. 935–945 (2013)Google Scholar
  9. 9.
    Wang, S., Jiang, J.: Learning natural language inference with LSTM (2015). arXiv:1512.08849
  10. 10.
    Wu, Y., Wu, W., Li, Z., Zhou, M.: Topic augmented neural network for short text conversation. CoRR abs/1605.00090 (2016)Google Scholar
  11. 11.
    Yan, R., Song, Y., Wu, H.: Learning to respond with deep neural networks for retrieval-based human-computer conversation system. In: SIGIR 2016, Pisa, Italy, pp. 55–64, 17–21 July 2016.
  12. 12.
    Zhou, X., Dong, D., Wu, H., Zhao, S., Yan, R., Yu, D., Liu, X., Tian, H.: Multiview response selection for human-computer conversation. In: EMNLP 2016 (2016)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information Technology, Madras Institute of TechnologyAnna UniversityChennaiIndia

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