Happiness Index in Social Network

  • Kuldeep SinghEmail author
  • Harish Kumar Shakya
  • Bhaskar Biswas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)


Happiness index in social networking sites shows the happiness of human being. For this dataset is collected from social network, Twitter and applied various sentiment analysis techniques to the data set. Positive Sentiment and Negative Sentiment is calculated based on the tweets. Tweets were retrieved based on location-wise (latitude and longitude) and country-wise. Consequently, maps are plotted based on these sentiments location-wise and country-wise. Individual users are also track, and their happiness is monitored over time to get an idea of how frequent their mood changes and their general happiness pattern. Proposed algorithm finds a change of the pattern of happiness. A user’s past tweets are also monitored and applied topic detection and the subjectivity extraction technique to get a more concrete idea of this pattern.


Twitter Social network Sentiment analysis Happiness index 


  1. 1.
    Dodds, P.S., Harris, K.D., Kloumann, I.M., Bliss, C.A., Danforth, C.M.: Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter. PLoS ONE 6(12), 2011. doi: 10.1371/journal.pone.0026752
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Kumar, S., Morstatter, F., Liu, H.: Twitter Data Analytics. Springer, New York (2014). doi: 10.1007/978-1-4614-9372-3. Series ISSN -2191-5768CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Narayanan, V., Arora, I., Bhatia, A.: Fast and accurate sentiment classification using an enhanced naive bayes model. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 194–201. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41278-3_24 CrossRefGoogle Scholar
  8. 8.
    Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of 5th Conference on Language Resources and Evaluation (LREC 2006), pp. 417–422 (2006)Google Scholar
  9. 9.
    Bolelli, L., Ertekin, Ş., Giles, C.L.: Topic and trend detection in text collections using latent dirichlet allocation. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 776–780. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-00958-7_84 CrossRefGoogle Scholar
  10. 10.
  11. 11.
  12. 12.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Kuldeep Singh
    • 1
    Email author
  • Harish Kumar Shakya
    • 1
  • Bhaskar Biswas
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (BHU)VaranasiIndia

Personalised recommendations