Happiness Index in Social Network
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.
KeywordsTwitter Social network Sentiment analysis Happiness index
- 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
- 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.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.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