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A Comparison of Pre-processing Techniques for Twitter Sentiment Analysis

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Research and Advanced Technology for Digital Libraries (TPDL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10450))

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Abstract

Pre-processing is considered to be the first step in text classification, and choosing the right pre-processing techniques can improve classification effectiveness. We experimentally compare 15 commonly used pre-processing techniques on two Twitter datasets. We employ three different machine learning algorithms, namely, Linear SVC, Bernoulli Naïve Bayes, and Logistic Regression, and report the classification accuracy and the resulting number of features for each pre-processing technique. Finally, based on our results, we categorize these techniques based on their performance. We find that techniques like stemming, removing numbers, and replacing elongated words improve accuracy, while others like removing punctuation do not.

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Notes

  1. 1.

    http://noisy-text.github.io/.

  2. 2.

    http://norvig.com/spell-correct.html.

  3. 3.

    http://sentistrength.wlv.ac.uk.

  4. 4.

    http://alt.qcri.org/semeval2017/.

References

  1. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM 2011, Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 30–38 (2011). http://dl.acm.org/citation.cfm?id=2021109.2021114

  2. Bird, S.: NLTK: the natural language toolkit. In: Calzolari, N., Cardie, C., Isabelle, P. (eds.) ACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney, Australia, 17–21 July 2006. The Association for Computer Linguistics (2006). http://aclweb.org/anthology/p06-4018

  3. Cherkassky, V.: The nature of statistical learning theory. IEEE Trans. Neural Netw. 8(6), 1564 (1997). doi:10.1109/TNN.1997.641482

    Article  Google Scholar 

  4. Fayyad, U.M., Piatetsky-Shapiro, G., Uthurusamy, R.: Summary from the KDD-03 panel: data mining: the next 10 years. SIGKDD Explor. 5(2), 191–196 (2003). doi:10.1145/980972.981004

    Article  Google Scholar 

  5. John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: UAI 1995: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, Canada, 18–20 August 1995, pp. 338–345 (1995). https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=450&proceeding_id=11

  6. Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, 2–6 November 2009, pp. 375–384 (2009). http://doi.acm.org/10.1145/1645953.1646003

  7. Miller, G.A.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995). doi:10.1145/219717.219748

    Article  Google Scholar 

  8. Mohammad, S., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the 7th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2013, Atlanta, Georgia, USA, 14–15 June 2013, pp. 321–327 (2013). http://aclweb.org/anthology/S/S13/S13-2053.pdf

  9. Mohammad, S.M., Zhu, X., Kiritchenko, S., Martin, J.D.: Sentiment, emotion, purpose, and style in electoral tweets. Inf. Process. Manage. 51(4), 480–499 (2015). doi:10.1016/j.ipm.2014.09.003

    Article  Google Scholar 

  10. Mullen, T., Malouf, R.: A preliminary investigation into sentiment analysis of informal political discourse. In: Computational Approaches to Analyzing Weblogs, Papers from the 2006 AAAI Spring Symposium, Technical Report SS-06-03, Stanford, California, USA, 27–29 March 2006, pp. 159–162 (2006). http://www.aaai.org/Library/Symposia/Spring/2006/ss06-03-031.php

  11. Na, J.C., Sui, H., Khoo, C., Chan, S., Zhou, Y.: Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews. In: Conference of the International Society for Knowledge Organization (ISKO), pp. 49–54 (2004)

    Google Scholar 

  12. Nakov, P., Rosenthal, S., Kozareva, Z., Stoyanov, V., Ritter, A., Wilson, T.: SemEval-2013 task 2: sentiment analysis in twitter. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 2, pp. 312–320. Association for Computational Linguistics, Atlanta, Georgia, USA, June 2013. http://www.aclweb.org/anthology/S13-2052

  13. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011). http://dl.acm.org/citation.cfm?id=2078195

    MathSciNet  MATH  Google Scholar 

  14. Perkins, J.: Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing, Birmingham (2010)

    Google Scholar 

  15. Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980). doi:10.1108/eb046814

    Article  Google Scholar 

  16. Prasad, S.: Micro-blogging sentiment analysis using bayesian classification methods. Technical report (2010)

    Google Scholar 

  17. Saif, H., Fernández, M., He, Y., Alani, H.: Evaluation datasets for twitter sentiment analysis: a survey and a new dataset, the STS-gold. In: Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013) A Workshop of the XIII International Conference of the Italian Association for Artificial Intelligence (AI*IA 2013), Turin, Italy, 3 December 2013, pp. 9–21 (2013). http://ceur-ws.org/Vol-1096/paper1.pdf

  18. Shi, Y., Xi, Y., Wolcott, P., Tian, Y., Li, J., Berg, D., Chen, Z., Herrera-Viedma, E., Kou, G., Lee, H., Peng, Y., Yu, L. (eds.): Proceedings of the First International Conference on Information Technology and Quantitative Management, ITQM 2013, Dushu Lake Hotel, Sushou, China, 16–18 May 2013, Procedia Computer Science, vol. 17. Elsevier (2013). http://www.sciencedirect.com/science/journal/18770509/17

  19. Singh, T., Kumari, M.: Role of text pre-processing in twitter sentiment analysis. Proc. Comput. Sci. 89, 549–554 (2016). http://www.sciencedirect.com/science/article/pii/S1877050916311607

    Article  Google Scholar 

  20. Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. JASIST 63(1), 163–173 (2012). doi:10.1002/asi.21662

    Article  Google Scholar 

  21. Uysal, A.K., Günal, S.: The impact of preprocessing on text classification. Inf. Process. Manage. 50(1), 104–112 (2014). doi:10.1016/j.ipm.2013.08.006

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Database and Information Retrieval Research Unit, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100, Xanthi, Greece

    Dimitrios Effrosynidis, Symeon Symeonidis & Avi Arampatzis

Authors
  1. Dimitrios Effrosynidis
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  2. Symeon Symeonidis
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  3. Avi Arampatzis
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Corresponding author

Correspondence to Dimitrios Effrosynidis .

Editor information

Editors and Affiliations

  1. Faculteit der Geesteswetenschappen, Universiteit van Amsterdam , Amsterdam, The Netherlands

    Jaap Kamps

  2. Library & Information Center, University of Patras , Patras, Greece

    Giannis Tsakonas

  3. Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  4. Civil Engineering, University of Thrace , Kimmeria, Greece

    Lazaros Iliadis

  5. Informatics, Ionian University , Kerkyra, Greece

    Ioannis Karydis

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Effrosynidis, D., Symeonidis, S., Arampatzis, A. (2017). A Comparison of Pre-processing Techniques for Twitter Sentiment Analysis. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2017. Lecture Notes in Computer Science(), vol 10450. Springer, Cham. https://doi.org/10.1007/978-3-319-67008-9_31

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  • DOI: https://doi.org/10.1007/978-3-319-67008-9_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67007-2

  • Online ISBN: 978-3-319-67008-9

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