Publication Bibtex

NILC_USP: A Hybrid System for Sentiment Analysis in Twitter Messages (bibtex)
by Balage Filho, Pedro Paulo; Pardo, Thiago Alexandre Salgueiro
Abstract:
This paper describes the NILC USP system that participated in SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system adopts a hybrid classification process that uses three classification approaches: rule-based, lexicon-based and machine learning approaches. We suggest a pipeline architecture that extracts the best characteristics from each classifier. Our system achieved an F- score of 56.31% in the Twitter message-level subtask.

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Reference:
P. P. Balage Filho, T. A. S. Pardo, "NILC_USP: A Hybrid System for Sentiment Analysis in Twitter Messages", in Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), Atlanta, Georgia, USA: Association for Computational Linguistics, 2013, pp. 568-572.
Bibtex Entry:
@InProceedings{BalageFilho2013NILCUSPHybridSystem,
  Title                    = {{NILC\_USP}: A Hybrid System for Sentiment Analysis in Twitter Messages},
  Author                   = {Balage Filho, Pedro Paulo and Pardo, Thiago Alexandre Salgueiro},
  Booktitle                = {Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)},
  Year                     = {2013},

  Address                  = {Atlanta, Georgia, USA},
  Month                    = {14--15~} # june,
  Pages                    = {568--572},
  Publisher                = {Association for Computational Linguistics},

  Abstract                 = {This paper describes the NILC USP system that participated in SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system adopts a hybrid classification process that uses three classification approaches: rule-based, lexicon-based and machine learning approaches. We suggest a pipeline architecture that extracts the best characteristics from each classifier. Our system achieved an F- score of 56.31\% in the Twitter message-level subtask.},
  PDF                      = {http://www.pedrobalage.com/pubs/BalageFilho2013NILCUSPHybridSystem.pdf},
  SourceCode               = {https://github.com/pedrobalage/SemevalTwitterHybridClassifier2013}
}
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