Measuring Opinion Credibility in Twitter-Reference-Cited by-同舟云学术

Measuring Opinion Credibility in Twitter

Published:2015 Issue: Volume: Page:205-214
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Thandar Mya,Usanavasin Sasiporn

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-19024-2_21

Reference12 articles.

1. Bafna, K., Toshniwal, D.: Feature Based Summarization of Customers’ Reviews of Online Products. In: 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, vol. 22, pp. 142–151 (2013)

2. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford (2009)

3. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM Press, New York (2004)

4. Liang, C., Liu, Z., Sun, M.: Expert Finding for Microblog Misinformation Identification. In: Proceedings of COLING, Mumbai (2012)

5. Liang, P.W., Dai, B.R.: Opinion Mining on Social Media Data. In: IEEE 14th International Conference on Mobile Data Management (MDM), pp. 91–96. IEEE Press, Milan (2013)

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive survey on sentiment analysis: Challenges and future insights;Journal of Intelligent & Fuzzy Systems;2022-11-11

2. Framework for Fake News Classification Using Vectorization and Machine Learning;Studies in Computational Intelligence;2021-12-16

3. Diabetes Prediction and Classification using Machine Learning Algorithms;2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON);2021-12-04

4. A Study on Discernment of Fake News Using Machine Learning Algorithms;Evolutionary Computing and Mobile Sustainable Networks;2020-08-01