Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Reference :

    1. Chakraborty, K., Bhattacharyya, S., & Bag, R. (2020). A survey of sentiment analysis from social media data. IEEE Transactions on Computational Social Systems, 7(2), 450-464
    2. Khan, F. H., Qamar, U., & Bashir, S. (2016). SentiMI:Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection. Applied Soft Computing, 39, 140-153
    3. Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 8, No. 1).
    4. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307
    5. Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121
    6. Lin, H., Jia, J., Guo, Q., Xue, Y., Li, Q., Huang, J., ... & Feng, L. (2014, November). User-level psychological stress detection from social media using deep neural network. In Proceedings of the22nd ACM international conference on Multimedia (pp. 507-516).
    7. Guntuku, S. C., Buffone, A., Jaidka, K., Eichstaedt, J. C., & Ungar, L. H. (2019, July). Understanding and measuring psychological stress using social media. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, pp. 214-225).
    8. Hu, X., Tang, L., Tang, J., & Liu, H. (2013, February). Exploiting social relations for sentiment analysis in microblogging. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 537-546).
    9. Gopalakrishna Pillai, R., Thelwall, M., & Orasan, C. (2018, April). Detection of stress and relaxation magnitudes for tweets. In Companion Proceedings of the The Web Conference 2018 (pp. 1677-1684).
    10. Hasan, A., Moin, S., Karim, A., & Shamshirband, S. (2018). Machine learning-based sentiment analysis for twitter accounts. Mathematical and Computational Applications, 23(1), 11.
    11. Yuan, J., You, Q., & Luo, J. (2015). Sentiment analysis using social multimedia. In Multimedia data mining and analytics (pp. 31-59). Springer, Cham
    12. Ortega, R., Fonseca, A., & Montoyo, A. (2013, June). SSA- UO: unsupervised Twitter sentiment analysis. In Second joint conference on lexical and computational semantics (* SEM) (Vol. 2, pp. 501- 507)

Recent Article