Artificially Talented Architecture for Theme Detection

  • A. KaramchandaniEmail author
  • T. Agey
  • A. Chavan
  • Vaibhav Khatavkar
  • Parag Kulkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


Intelligent systems are the need of today’s world. Collections of data and various data sets are made available to naive users. Understanding what is contained within the dataset is quite difficult by referring just the name. Some of the datasets have quite a difficult, weird names so users do not have any clue what is inside, so there is a need of the theme of the document or dataset so as to understand what are the contents. User satisfaction and convenience is of prime importance. In this paper, we try to propose a system along with a working prototype of such intelligent system that essentially is a Chatbot which uses facility of Theme Detection in semantic analysis stage while processing the user input. This makes the system more productive. This paper talks about Chatbot and improvement in intelligent responses using theme detection. We have built a prototype of the system.


Intelligent system Chatbot Theme detection Semantic analysis Context vector Text analysis Vector space model 


  1. 1.
    Bawakid, A.: Using wikipedia categories for discovering the themes of text documents. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 1, pp. 452–455, Aug 2015Google Scholar
  2. 2.
    Das, A., Bandyopadhyay, S.: Theme detection an exploration of opinion subjectivity. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–6, Sept 2009Google Scholar
  3. 3.
    Quintero, J., Asprilla, R.: Towards an efficient voice-based chatbot. In: 2015 IEEE Institute of Electrical and Electronics Engineers Thirty Fifth Central American and Panama Convention (CONCAPAN XXXV), pp. 1–6, Nov 2015Google Scholar
  4. 4.
    Angga, P.A., Fachri, W.E., Elevanita, A., Suryadi, Agushinta, R.D.: Design of chatbot with 3D avatar, voice interface, and facial expression. In: 2015 International Conference on Science in Information Technology (ICSITech), pp. 326–330, Oct 2015Google Scholar
  5. 5.
    Kaushik, D., Kumar, D: A review paper on holographic projection. IJIRT Int. J. Innov. Res. Technol. 1(6), 1–8 (2014)Google Scholar
  6. 6.
    Richardson, M.J., Wiltshire, J.D.: What is a Hologram? Wiley, pp. 336. IEEE—Institute of Electrical and Electronics Engineers—Press, (2018)Google Scholar
  7. 7.
    Kulkarni, P., Khatavkar, V.: Context Vector Machine for Information Retrieval, vol. 137Google Scholar
  8. 8.
    Rodrigo, S.M., Abraham, J.G.F.: Development and implementation of a chat bot in a social network. pp. 751–755, Apr 2012Google Scholar
  9. 9.
    Wallace, R.S.: The Anatomy of A.L.I.C.E., pp. 181–210. Springer Netherlands, Dordrecht (2009)CrossRefGoogle Scholar
  10. 10.
    Apple Inc. iOS—Siri—Apple (2017). Accessed 05 Dec 2017
  11. 11.
    Google. Google assistant—your own personal Google (2017). Accessed 05 Dec 2017
  12. 12.
    Amazon (2017). Accessed 05 Dec 2017
  13. 13.
    DuckDuckGo Inc. (2017). Accessed 05 Dec 2017
  14. 14.
    Pariser, E.: The Filter Bubble: What the Internet is Hiding from You, vol. 137Google Scholar
  15. 15.
    Takuwa, K., Yoshikawa, T., Jimenez, F., Furuhashi, T.: A study on document classification using multiple distributed representations. pp. 1–4, June 2017Google Scholar
  16. 16.
    Qian, T., Sheu, P.C.Y., Li, S., Wang, L.: A scientific theme emergence detection approach based on citation graph analysis. vol. 2, pp. 269–273, Nov 2008Google Scholar
  17. 17.
    Liu, Z., Zhang, W., Sun, J., Cheng, H.N.H., Peng, X., Liu, S.: Emotion and associated topic detection for course comments in a MOOC platform. pp. 15–19, Sept 2016Google Scholar
  18. 18.
    Li, H., Li, Q.: Forum topic detection based on hierarchical clustering. pp. 529–533, July 2016Google Scholar
  19. 19.
    Nassar, L., Ibrahim, R., Karray, F.: Enhancing topic detection in twitter using the crowdsourcing process. pp. 196–203, Oct 2016Google Scholar
  20. 20.
    Chen, Y., Liu, L.: Development and research of topic detection and tracking, pp. 170–173, Aug 2016Google Scholar
  21. 21.
    Wibowo, F.W., Setiaji, B.: Chatbot using a knowledge in database. In: 7th International Conference on Intelligent Systems, Modelling and Simulation, vol. 2016Google Scholar
  22. 22.
    Manekiya, M.H., Arulmozhivarman, P.: 3D volume reconstruction using hologram. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1570– 1574, Apr 2016Google Scholar
  23. 23.
    gunthercox. GitHub—gunthercox/chatterbot-corpus: a multilingual dialog corpus (2017). Accessed 20 Dec 2017
  24. 24.
    Loper, E., Bird, S., Klein, E.: Natural language toolkit—NLTK 3.2.5 documentation (2007). Accessed 20 Dec 2017
  25. 25.
    Stanford NLP Group. The Stanford Natural Language Processing Group. Accessed 20 Dec 2017
  26. 26.
    Behera, B.: Chappie—a semi-automatic intelligent chatbotGoogle Scholar
  27. 27.
    Setiaji, B., Wibowo, F.W.: Chatbot using a knowledge in database: human-to-machine conversation modeling. In: 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 72–77, Jan 2016Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • A. Karamchandani
    • 1
    Email author
  • T. Agey
    • 1
  • A. Chavan
    • 1
  • Vaibhav Khatavkar
    • 1
  • Parag Kulkarni
    • 1
  1. 1.Department of Computer Engineering and IT, College of EngineeringPuneIndia

Personalised recommendations