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Volumn WS-11-05, Issue , 2011, Pages 44-49

Domain adaptation in sentiment analysis of twitter

Author keywords

[No Author keywords available]

Indexed keywords

DATA SOURCE; DIFFERENT DOMAINS; DOMAIN ADAPTATION; EXPECTATION MAXIMIZATION; ITERATIVE ALGORITHM; MUTUAL INFORMATIONS; NEGATIVE SENTIMENTS; NOISY DATA; ROCCHIO; SENTIMENT ANALYSIS;

EID: 80054958074     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (42)

References (23)
  • 2
    • 78650122641 scopus 로고    scopus 로고
    • Twitter sentiment classification using distant supervision
    • A.Go, et al. 2009. Twitter sentiment classification using distant supervision, CS224N Project Report,Stanford.
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    • Go, A.1
  • 5
    • 77955075329 scopus 로고    scopus 로고
    • Sentiment analysis: A multi-faceted problem
    • Invited contribution to
    • B. Liu. 2010. Sentiment analysis: A multi-faceted problem, Invited contribution to IEEE Intelligent System.
    • (2010) IEEE Intelligent System
    • Liu, B.1
  • 7
    • 84944949595 scopus 로고
    • The effect of adding relevance information in a relevance feedback environment
    • C. Buckley, et.al. 1994. The effect of adding relevance information in a relevance feedback environment, SIGIR94.
    • (1994) SIGIR94
    • Buckley, C.1
  • 12
    • 84880798303 scopus 로고    scopus 로고
    • Learning to Classify Texts Using Positiveand Unlabeled Data
    • X. Li, B. Liu. 2003. Learning to Classify Texts Using Positiveand Unlabeled Data, IJCAI-33.
    • (2003) IJCAI-33
    • Li, X.1    Liu, B.2
  • 13
    • 77954168866 scopus 로고    scopus 로고
    • Characterizing Debate Performance via Aggregated Twitter Sentiment
    • N. A. Diakopoulos, et.al. 2010. Characterizing Debate Performance via Aggregated Twitter Sentiment, CHI 2010.
    • (2010) CHI 2010
    • Diakopoulos, N.A.1
  • 17
    • 77950911780 scopus 로고    scopus 로고
    • Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis, ECIR 2009
    • S. Tan, et al. 2009. Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis, ECIR 2009, LNCS 5478, pp. 337-349.
    • (2009) LNCS , vol.5478 , pp. 337-349
    • Tan, S.1
  • 20
    • 85112593657 scopus 로고    scopus 로고
    • Sentiment analysis using support vector machines with diverse information sources
    • Barcelona, Spain
    • T. Mullen, N. Collier. 2004. Sentiment analysis using support vector machines with diverse information sources, In Proceedings of EMNLP-2004, pages 412-418, Barcelona, Spain.
    • (2004) Proceedings of EMNLP-2004 , pp. 412-418
    • Mullen, T.1    Collier, N.2
  • 21
    • 31844440904 scopus 로고    scopus 로고
    • Beyond the point cloud: From transductive to semi supervised learning
    • V. Sindhwani, et al. 2005. Beyond the point cloud: From transductive to semi supervised learning, International Conference on Machine Learning.
    • (2005) International Conference on Machine Learning
    • Sindhwani, V.1
  • 23
    • 79952082709 scopus 로고    scopus 로고
    • Thailand-Tourism and Conflict: Modeling Sentiment from Twitter Tweets using Naive Bayes and Unsupervised Neural Nets
    • W. B. Claster, et al. 2010. Thailand-Tourism and Conflict: Modeling Sentiment from Twitter Tweets using Naive Bayes and Unsupervised Neural Nets, CIMSim2010: Computational Intelligence, Modeling and Simulation.
    • (2010) CIMSim2010: Computational Intelligence, Modeling and Simulation
    • Claster, W.B.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.