Open Access
Issue
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
Article Number 04046
Number of page(s) 7
Section Computer Science
DOI https://doi.org/10.1051/e3sconf/202339904046
Published online 12 July 2023
  1. Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10) (pp. 2200–2204). [Google Scholar]
  2. Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1(2009), 12. [Google Scholar]
  3. Li, J., Cardie, C., & Ji, H. (2012). Mining point-wise mutual information from tweet streams. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2 (pp. 381–385). Association for Computational Linguistics. [Google Scholar]
  4. Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level convolutional networks for text classification. In Advances in neural information processing systems (pp. 649–657). [Google Scholar]
  5. Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media?. In Proceedings of the 19th International Conference on World Wide Web (pp. 591–600). [CrossRef] [Google Scholar]
  6. Pennacchiotti, M., & Popescu, A.M. (2011). Democrats, Republicans and Starbucks Afficionados: User classification in Twitter. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 430–438). [CrossRef] [Google Scholar]
  7. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1–135. [CrossRef] [Google Scholar]
  8. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1–167. [CrossRef] [Google Scholar]
  9. Shu, K., Mahudeswaran, D., Wang, S., & Lee, D. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36. [CrossRef] [Google Scholar]
  10. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. [CrossRef] [Google Scholar]
  11. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. [Google Scholar]
  12. Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems handbook. Springer. [CrossRef] [Google Scholar]
  13. Newman, M.E. (2010). Networks: an introduction. Oxford University Press. [CrossRef] [Google Scholar]
  14. Leskovec, J., & Krevl, A. (2014). SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data. [Google Scholar]
  15. Becker, H., Naaman, M., & Gravano, L. (2011). Beyond trending topics: Real-world event identification on Twitter. In Fifth international AAAI conference on weblogs and social media. [Google Scholar]
  16. Petrovic, S., Osborne, M., & Lavrenko, V. (2010). Streaming first story detection with application to Twitter. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 181–189). [Google Scholar]
  17. Romero, D.M., Meeder, B., & Kleinberg, J. (2011). Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on Twitter. In Proceedings of the 20th international conference on World Wide Web (pp. 695–704). [CrossRef] [Google Scholar]
  18. Lerman, K., & Hogg, T. (2010). Using a model of social dynamics to predict popularity of news. In Proceedings of the 19th International Conference on World Wide Web (pp. 621–630). [CrossRef] [Google Scholar]
  19. Tsai, C.F., Wang, S.W., Huang, Y.M., & Tseng, S.S. (2014). Predicting user engagement on social media: a data perspective. Social Network Analysis and Mining, 4(1), 1–18. [CrossRef] [Google Scholar]
  20. Yang, J., Counts, S., & Hoff, A. (2011). Predicting the speed, scale, and range of information diffusion in Twitter. In Proceedings of the Fourth International Conference on Weblogs and Social Media. [Google Scholar]
  21. Dinakar, J.R., & Vagdevi, S. (2023). Real-time streaming analytics using big data paradigm and predictive modelling based on deep learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11, 161–165. doi: 10.17762/ijritcc.v11i4s.6323 [CrossRef] [Google Scholar]
  22. Bai, V.S., & Sudha, T. (2023). A systematic literature review on cloud forensics in cloud environment. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 565–578. Retrieved from www.scopus.com [Google Scholar]
  23. Lopez, M., Popovic, N., Dimitrov, D., Botha, D., & Ben-David, Y. Efficient Dimensionality Reduction Techniques for High-Dimensional Data. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/145 [Google Scholar]
  24. Dwarkanath Pande, S., & Hasane Ahammad, D.S.. (2022). Cognitive ComputingBased Network Access Control System in Secure Physical Layer. Research Journal of Computer Systems and Engineering, 3(1), 14–20. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/36 [Google Scholar]
  25. Dhabliya, D. (2021). An Integrated Optimization Model for Plant Diseases Prediction with Machine Learning Model . Machine Learning Applications in Engineering Education and Management, 1(2), 21–26. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/15 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.