Sentiment Analysis and Mood Detection on an Android Platform Using Machine Learning Integrated with Internet of Things

  • Diksha Kushawaha
  • Debalina De
  • Vandana MohindruEmail author
  • Anuj Kumar Gupta
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)


Mental health is considered as one of the most sensitive topics of research and it is highly affected by an individual’s mood and sentiments. Social media has been proven to be one of the major catalysts in deterioration and fickleness of one’s mind. In this paper, we present an android application called “moody buddy” ingratiated with a heartbeat analyzing hardware kit which would detect and analyze the moods and emotions of an individual very close to accuracy. Mood recognition and sentiment analysis is a vast and complex area of research. Moreover, monitoring human emotions is found out to be one of the technically challenging aspects. So, in order to achieve the quality output of our research and testing work, we have taken help from artificial intelligence and Internet of Things domain. Here, we have considered the activity of the user on his/her social networking as a starting point of our research work. The concept of logistic regression is used in our software. In order to solidify our idea more, we are adding a hardware component which would monitor the heartbeat of the person and its modulation. In case of any abnormality examined in the heart rate, the questionnaire appears again. At the end, a cumulative of the hardware component’s results and software component’s would help us analyze and detect the current mood of the individual to very close to high accuracy value.


Mood and sentiment analysis Artificial intelligence Internet of Things Heart rate monitoring IBM Watson Bag of words 


  1. 1.
    Grover, S., Dutt, A., Avasthi, A.: An overview of Indian research in depression. Indian J. Psychiatry 52(Suppl1), S178 (2010)Google Scholar
  2. 2.
  3. 3.
    Byrne, G.J., Pachana, N.A.: Anxiety and depression in the elderly: do we know any more? Curr. Opin. Psychiatry 23(6), 504–509 (2010)CrossRefGoogle Scholar
  4. 4.
    Lev, I., Shalom, E., Avidor, A.: Hierarchic model and natural language analyzer. U.S. Patent Application 15/275,620, filed March 29, 2018Google Scholar
  5. 5.
  6. 6.
    Castro, D., New, J.: The Promise of Artificial Intelligence. Center for Data Innovation (2016)Google Scholar
  7. 7.
    Chung, H., Iorga, M., Voas, J., Lee, S.: Alexa, can i trust you? Computer 50(9), 100–104 (2017)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Zięba, M., Tomczak, J.M., Brzostowski, K.: Selecting right questions with restricted Boltzmann machines. In: Progress in Systems Engineering, pp. 227–232. Springer, Cham (2015)Google Scholar
  10. 10.
    Golzadeh, H., Ekárt, A., Faria, D.R., Buckingham, C.D., Manso, L.J.: Emotion recognition using spatiotemporal features from facial expression landmarksGoogle Scholar
  11. 11.
    Connor Adams Sheets, December: How Does Akinator Work? Behind the Genie that “Reads Your Mind” (2018)Google Scholar
  12. 12.
    Gonsior, B., Sosnowski, S., Buß, M., Wollherr, D., Kühnlenz, K.: An emotional adaption approach to increase helpfulness towards a robot. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2429–2436. IEEE (2012)Google Scholar
  13. 13.
    Kühnlenz, B., Sosnowski, S., Buß, M., Wollherr, D., Kühnlenz, K., Buss, M.: Increasing helpfulness towards a robot by emotional adaption to the user. Int. J. Soc. Robot. 5(4), 457–476 (2013)CrossRefGoogle Scholar
  14. 14.
    Toxtli, C., Monroy-Hernández, A., Cranshaw, J.: Understanding chatbot-mediated task management. In: Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’18), Paper 58, 6 pages. ACM, New York, NY, USA (2018).
  15. 15.
  16. 16.
    Chen, Y., Argentinis, J.D.E., Weber, G.: IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin. Ther. 38(4), 688–701 (2016)Google Scholar
  17. 17.
    Müller, O., Junglas, I., Debortoli, S., vom Brocke, J.: Using text analytics to derive customer service management benefits from unstructured data. MIS Q. Executive 15(4), 243–258 (2016)Google Scholar
  18. 18.
  19. 19.
    Nowak, J., Taspinar, A., Scherer, R.: LSTM recurrent neural networks for short text and sentiment classification. In: International Conference on Artificial Intelligence and Soft Computing, pp. 553–562. Springer, Cham (2017)Google Scholar
  20. 20.
    Ke, H., Shaoping, M.: Text categorization based on. Concept indexing and principal component analysis. In: 2002 IEEE Region 10 Conference on Computers, Communications, Control, and Power Engineering. TENCOM’02 Proceedings, vol. 1, pp. 51–56. IEEE (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Diksha Kushawaha
    • 1
  • Debalina De
    • 1
  • Vandana Mohindru
    • 1
    Email author
  • Anuj Kumar Gupta
    • 1
  1. 1.Department of Computer Science and EngineeringChandigarh Group of Colleges - College of EngineeringMohaliIndia

Personalised recommendations