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Sentiment Analysis in Turkish

  • Gizem Gezici
  • Berrin YanıkoğluEmail author
Chapter
Part of the Theory and Applications of Natural Language Processing book series (NLP)

Abstract

In this chapter, we give an overview of sentiment analysis problem and present a system to estimate the sentiment of movie reviews in Turkish. Our approach combines supervised learning and lexicon-based approaches, making use of a recently constructed Turkish polarity lexicon called SentiTurkNet. For performance evaluation, we investigate the contribution of different feature sets, as well as the effect of lexicon size on the overall classification performance.

References

  1. Akın AA, Akın MD (2007) Zemberek, an open source NLP framework for Turkic languages. Structure 10:1–5Google Scholar
  2. Bespalov D, Bai B, Qi Y, Shokoufandeh A (2011) Sentiment classification based on supervised latent n-gram analysis. In: Proceedings of the ACM international conference on information and knowledge management, Glasgow, pp 375–382Google Scholar
  3. Bespalov D, Qi Y, Bai B, Shokoufandeh A (2012) Sentiment classification with supervised sequence embedding. In: Proceedings of conference on machine learning and knowledge discovery in databases, Bristol, pp 159–174Google Scholar
  4. Bilgin O, Çetinoğlu Ö, Oflazer K (2004) Building a Wordnet for Turkish. Rom J Inf Sci Technol 7(1–2):163–172Google Scholar
  5. Boynukalın Z (2012) Emotion analysis of Turkish texts by using machine learning methods. Master’s thesis, Middle East Technical University, AnkaraGoogle Scholar
  6. Çakmak O, Kazemzadeh A, Yıldırım S, Narayanan S (2012) Using interval type-2 fuzzy logic to analyze Turkish emotion words. In: Proceedings of the annual summit and conference of signal information processing association, Los Angeles, CA, pp 1–4Google Scholar
  7. Cambria E, Speer R, Havasi C, Hussain A (2010) Senticnet: a publicly available semantic resource for opinion mining. In: Proceedings of AAAI fall symposium: commonsense knowledge, Arlington, VA, vol 10, p 02Google Scholar
  8. Dehkharghani R, Saygın Y, Yanıkoğlu B, Oflazer K (2016) SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Lang Resour Eval 50(3):667–685Google Scholar
  9. Demiröz G, Yanıkoğlu B, Tapucu D, Saygın Y (2012) Learning domain-specific polarity lexicons. In: Proceedings of the workshop on sentiment elicitation from natural text for information retrieval and extraction, Brussels, pp 674–679Google Scholar
  10. Demirtaş E, Pechenizkiy M (2013) Cross-lingual polarity detection with machine translation. In: Proceedings of the international workshop on issues of sentiment discovery and opinion mining, Chicago, IL, pp 9:1–9:8Google Scholar
  11. Eroğul U (2009) Sentiment analysis in Turkish. Master’s thesis, Middle East Technical University, AnkaraGoogle Scholar
  12. Esuli A, Sebastiani F (2006) Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of LREC, Genoa, vol 6, pp 417–422Google Scholar
  13. Fellbaum C (1998) WordNet: an electronic lexical database. MIT Press, Cambridge, MAGoogle Scholar
  14. Gezici G, Yanıkoğlu B, Tapucu D, Saygın Y (2012) New features for sentiment analysis: do sentences matter? In: Proceedings of the International Workshop on Sentiment Discovery from Affective Data, Bristol, pp 5–15Google Scholar
  15. Ghorbel H, Jacot D (2011) Sentiment analysis of French movie reviews. In: Advances in distributed agent-based retrieval tools. Springer, BerlinGoogle Scholar
  16. Hagen M, Potthast M, Büchner M, Stein B (2015) Webis: an ensemble for Twitter sentiment detection. In: Proceedings of SEMEVAL, Denver, CO, pp 582–589Google Scholar
  17. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18Google Scholar
  18. Hatzivassiloglou V, McKeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of ACL-EACL, Madrid, pp 174–181Google Scholar
  19. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, pp 168–177Google Scholar
  20. Kaya M (2013) Sentiment analysis of Turkish political columns with transfer learning. PhD thesis, Middle East Technical University, AnkaraGoogle Scholar
  21. Kaya M, Fidan G, Toroslu İH (2012) Sentiment analysis of Turkish political news. In: Proceedings of the 2012 IEEE/WIC/ACM international joint conferences on web intelligence and intelligent agent technology, Macau, pp 174–180Google Scholar
  22. Mao Y, Lebanon G (2006) Isotonic conditional random fields and local sentiment flow. In: Proceedings of NIPS, Vancouver, pp 961–968Google Scholar
  23. Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of ACL, Barcelona, pp 271–278Google Scholar
  24. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135Google Scholar
  25. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of EMNLP, Philadelphia, PA, pp 79–86Google Scholar
  26. Poria S, Gelbukh A, Cambria E, Das D, Bandyopadhyay S (2012) Enriching SenticNet polarity scores through semi-supervised fuzzy clustering. In: Proceedings of the workshop on sentiment elicitation from natural text for information retrieval and extraction, Brussels, pp 709–716Google Scholar
  27. Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27Google Scholar
  28. Rosenthal S, Ritter A, Nakov P, Stoyanov V (2014) Semeval-2014 task 9: sentiment analysis in twitter. In: Proceedings of SEMEVAL, Dublin, pp 73–80Google Scholar
  29. Severyn A, Moschitti A (2015) UNITN: training deep convolutional neural network for Twitter sentiment classification. In: Proceedings of SEMEVAL, Denver, CO, pp 464–469Google Scholar
  30. Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of EMNLP, Seattle, WA, pp 1631–1642Google Scholar
  31. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307Google Scholar
  32. Tang D, Wei F, Qin B, Liu T, Zhou M (2014) Coooolll: a deep learning system for twitter sentiment classification. In: Proceedings of SEMEVAL, Dublin, pp 208–212Google Scholar
  33. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558Google Scholar
  34. Türkmenoğlu C, Tantuğ AC (2014) Sentiment analysis in Turkish media. Technical report, Istanbul Technical University, IstanbulGoogle Scholar
  35. Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of ACL, Philadelphia, PA, pp 417–424Google Scholar
  36. Vural AG, Cambazoğlu BB, Şenkul P, Tokgöz ZÖ (2013) A framework for sentiment analysis in Turkish: application to polarity detection of movie reviews in Turkish. In: Proceedings of ISCIS, Paris, pp 437–445Google Scholar
  37. Wiebe J (2000) Learning subjective adjectives from corpora. In: Proceedings of AAAI, Austin, TX, pp 735–740Google Scholar
  38. Wiebe J, Wilson T, Bruce R, Bell M, Martin M (2004) Learning subjective language. Comput Linguist 30(3):277–308Google Scholar
  39. Wilson T, Wiebe J, Hwa R (2004) Just how mad are you? Finding strong and weak opinion clauses. In: Proceedings of AAAI, San Jose, CA, pp 761–769Google Scholar
  40. Zhao J, Liu K, Wang G (2008) Adding redundant features for CRF-based sentence sentiment classification. In: Proceedings of EMNLP, Honolulu, HI, pp 117–126Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Sabancı UniversityIstanbulTurkey

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