Automatic emotion detection in text streams by analyzing Twitter data

Author:

Hasan Maryam,Rundensteiner Elke,Agu Emmanuel

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Modeling and Simulation,Information Systems

Reference43 articles.

1. Wang, W., Chen, L., Thirunarayan, K., Sheth, AP.: Harnessing twitter big data for automatic emotion identification. In: 2012 International Conference on Social Computing (SocialCom), pp 587–592. IEEE (2012)

2. De Choudhury, M., Counts, S., Gamon, M.: Not all moods are created equal! exploring human emotional states in social media. In: ICWSM’12 (2012)

3. Wakamiya, S., Belouaer, L., Brosset, D., Lee, R., Kawai, Y., Sumiya, K., Claramunt, C.: Measuring crowd mood in city space through twitter. In: International Symposium on Web and Wireless Geographical Information Systems, pp 37–49. Springer (2015)

4. Choudhury, MD., Gamon, M., Counts,S., Horvitz, E.: Predicting depression via social media. In: ICWSM’13, The AAAI Press (2013)

5. Park, M., Cha, C., Cha, M .: (2012) Depressive moods of users portrayed in twitter. In: Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics, HI-KDD

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