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Volumn 2018-January, Issue , 2018, Pages 1386-1393

Two simple and effective ensemble classifiers for Twitter sentiment analysis

Author keywords

Ensemble learners; Lack of training data; Twitter sentiment; Twitter sentiment analysis

Indexed keywords

SENTIMENT ANALYSIS; SOCIAL NETWORKING (ONLINE);

EID: 85046007143     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SAI.2017.8252275     Document Type: Conference Paper
Times cited : (12)

References (21)
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    • Kathy Lee, Ankit Agrawal, and Alok Choudhary. 2013. Real-time disease surveillance using Twitter data: demonstration on flu and cancer. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '13), Inderjit S. Dhillon, Yehuda Koren, Rayid Ghani, Ted E. Senator, Paul Bradley, Rajesh Parekh, Jingrui He, Robert L. Grossman, and Ramasamy Uthurusamy (Eds.). ACM, New York, NY, USA, 1474-1477. DOI=http://dx.doi.org/10.1145/2487575.2487709
    • (2013) Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '13) , pp. 1474-1477
    • Lee, K.1    Agrawal, A.2    Choudhary, A.3
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  • 9
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    • Sentiment classification: The contribution of ensemble learning
    • Wang, G., Sun, J., Ma, J., Xu, K., & Gu, J. (2014). Sentiment classification: The contribution of ensemble learning. Decision support systems, 57, 77-93
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    • Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens' political preferences with an application to Italy and France. New Media & Society, 16(2), 340-358.
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    • Mining brand perceptions from Twitter social networks
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    • Stanford
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.