Publication Bibtex

NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages (bibtex)
by Balage Filho, Pedro Paulo; Avanço, Lucas Vinicius; Nunes, Maria das Graças Volpe; Pardo, Thiago Alexandre Salgueiro
Abstract:
This paper describes the NILC USP system that participated in SemEval-2014 Task 9: Sentiment Analysis in Twitter, a re-run of the SemEval 2013 task under the same name. Our system is an improved version of the system that participated in the 2013 task. This system adopts a hybrid classification process that uses three classification approaches: rule-based, lexicon-based and machine learning. We suggest a pipeline architecture that extracts the best characteristics from each classifier. In this work, we want to verify how this hybrid approach would improve with better classifiers. The improved system achieved an F-score of 65.39% in the Twitter message-level subtask for 2013 dataset (+ 9.08% of improvement) and 63.94% for 2014 dataset.

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Reference:
P. P. Balage Filho, L. V. Avanço, M. d. G. V. Nunes, T. A. S. Pardo, "NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages", in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), P. Nakov, T. Zesch, Eds., Dublin, Ireland: Association for Computational Linguistics and Dublin City University, 2014, pp. 428-432.
Bibtex Entry:
@InProceedings{BalageFilho2014NILCUSPImprovedHybrid,
  Title                    = {{NILC\_USP}: An Improved Hybrid System for Sentiment Analysis in Twitter Messages},
  Author                   = {Balage Filho, Pedro Paulo and Avan\c{c}o, Lucas Vinicius and Nunes, Maria das Gra\c{c}as Volpe and Pardo, Thiago Alexandre Salgueiro},
  Booktitle                = {Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)},
  Year                     = {2014},

  Address                  = {Dublin, Ireland},
  Editor                   = {Nakov, Preslav and Zesch, Torsten},
  Month                    = {23--24~} # aug,
  Pages                    = {428--432},
  Publisher                = {Association for Computational Linguistics and Dublin City University},

  Abstract                 = {This paper describes the NILC USP system that participated in SemEval-2014 Task 9: Sentiment Analysis in Twitter, a re-run of the SemEval 2013 task under the same name. Our system is an improved version of the system that participated in the 2013 task. This system adopts a hybrid classification process that uses three classification approaches: rule-based, lexicon-based and machine learning. We suggest a pipeline architecture that extracts the best characteristics from each classifier. In this work, we want to verify how this hybrid approach would improve with better classifiers. The improved system achieved an F-score of 65.39\% in the Twitter message-level subtask for 2013 dataset (+ 9.08\% of improvement) and 63.94\% for 2014 dataset.},
  PDF                      = {http://www.pedrobalage.com/pubs/BalageFilho2014NILCUSPImprovedHybrid.pdf},
  Poster                   = {http://www.pedrobalage.com/pubs/BalageFilho2014NILCUSPImprovedHybridPoster.pdf},
  SourceCode               = {https://github.com/pedrobalage/TwitterHybridClassifier}
}
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