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Research Article
Adaptive Diffusion of Sensitive Information in Online Social Network
Hari Priya R1
Gowtham R2
Joy Mariya Rubert A3
12 Computer Technology, Bannari Amman Institute of Technology, Tamil Nadu, India. 3Computer Science, Bannari Amman Institute of Technology, Tamil Nadu, India.
Published Online: March-April 2023
Pages: 286-289
Cite this article
No DOIReferences
1. B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Found. Trends Inf. Retrieval, vol. 2, no. 1/2, pp. 1–135, 2016.
2. J. Bollen, H. Mao, and A. Pepe, “Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena,” in Proc. Int.
AAAI Conf. Weblogs social media, 2017, pp. 17–21.
3. B. O’Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, “From tweets to polls: Linking text sentiment to public opinion
time series,” in Proc. Int. AAAI Conf. Weblogs social media, 2016, pp. 122–129.
4. M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining,
2014, pp. 168–177.
5. T. Chen, R. Xu, Y. He, Y. Xia, and X. Wang, “Learning user and product distributed representations using a sequence model for
sentiment analysis,” IEEE Comput. Intell. Mag., vol. 11, no. 3, pp. 34–44, Aug. 2016.
6. Y. Wu, S. Liu, K. Yan, M. Liu, and F. Wu, “OpinionFlow: Visual analysis of opinion diffusion on social media,” IEEE Trans. Vis.
Comput. Graph., vol. 20, no. 12, pp. 1763– 1772, Dec. 2017.
7. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proc. ACL Conf.
Empirical Methods Natural Language Process., 2018, pp. 79–86.
8. A.Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” Stanford Univ., Stanford, CA, USA,
Project Rep. CS224N, pp. 1–12, 2019.
9. F. Wu, Y. Song, and Y. Huang, “Microblog sentiment classification with contextual knowledge regularization,” in Proc. 29th AAAI Conf. Artif. Intell., 2015, pp.2332–2338.
10. J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, boom-boxes and blenders: Trends adaptation for sentiment
classification,” in Proc. 45th Annu. Meeting Assoc. Comput. Linguistics, 2017, vol. 7, pp. 440–447
2. J. Bollen, H. Mao, and A. Pepe, “Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena,” in Proc. Int.
AAAI Conf. Weblogs social media, 2017, pp. 17–21.
3. B. O’Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, “From tweets to polls: Linking text sentiment to public opinion
time series,” in Proc. Int. AAAI Conf. Weblogs social media, 2016, pp. 122–129.
4. M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining,
2014, pp. 168–177.
5. T. Chen, R. Xu, Y. He, Y. Xia, and X. Wang, “Learning user and product distributed representations using a sequence model for
sentiment analysis,” IEEE Comput. Intell. Mag., vol. 11, no. 3, pp. 34–44, Aug. 2016.
6. Y. Wu, S. Liu, K. Yan, M. Liu, and F. Wu, “OpinionFlow: Visual analysis of opinion diffusion on social media,” IEEE Trans. Vis.
Comput. Graph., vol. 20, no. 12, pp. 1763– 1772, Dec. 2017.
7. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proc. ACL Conf.
Empirical Methods Natural Language Process., 2018, pp. 79–86.
8. A.Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” Stanford Univ., Stanford, CA, USA,
Project Rep. CS224N, pp. 1–12, 2019.
9. F. Wu, Y. Song, and Y. Huang, “Microblog sentiment classification with contextual knowledge regularization,” in Proc. 29th AAAI Conf. Artif. Intell., 2015, pp.2332–2338.
10. J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, boom-boxes and blenders: Trends adaptation for sentiment
classification,” in Proc. 45th Annu. Meeting Assoc. Comput. Linguistics, 2017, vol. 7, pp. 440–447
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