Skip to main content
SpringerLink
Log in
Book cover

Turkish Natural Language Processing pp 255–271Cite as

Sentiment Analysis in Turkish

  • Chapter
  • First Online:

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    www.tripadvisor.com (Accessed Sept. 14, 2017).

  2. 2.

    www.imdb.com (Accessed Sept. 14, 2017).

  3. 3.

    www.amazon.com (Accessed Sept. 14, 2017).

  4. 4.

    www.beyazperde.com (Accessed Sept. 14, 2017).

  5. 5.

    We label these as follows in this chapter: a—adjective, n—noun, v—verb, and b—adverb.

  6. 6.

    Reviewers on Beyazperde rate movies star ratings of 1–5 scale, in addition to the review they enter.

References

  • Akın AA, Akın MD (2007) Zemberek, an open source NLP framework for Turkic languages. Structure 10:1–5

    Google Scholar 

  • 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 375–382

    Google Scholar 

  • 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 159–174

    Google Scholar 

  • Bilgin O, Çetinoğlu Ö, Oflazer K (2004) Building a Wordnet for Turkish. Rom J Inf Sci Technol 7(1–2):163–172

    Google Scholar 

  • Boynukalın Z (2012) Emotion analysis of Turkish texts by using machine learning methods. Master’s thesis, Middle East Technical University, Ankara

    Google Scholar 

  • 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 1–4

    Google Scholar 

  • 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 02

    Google Scholar 

  • Dehkharghani R, Saygın Y, Yanıkoğlu B, Oflazer K (2016) SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Lang Resour Eval 50(3):667–685

    Google Scholar 

  • 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 674–679

    Google Scholar 

  • 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 9:1–9:8

    Google Scholar 

  • Eroğul U (2009) Sentiment analysis in Turkish. Master’s thesis, Middle East Technical University, Ankara

    Google Scholar 

  • Esuli A, Sebastiani F (2006) Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of LREC, Genoa, vol 6, pp 417–422

    Google Scholar 

  • Fellbaum C (1998) WordNet: an electronic lexical database. MIT Press, Cambridge, MA

    Google Scholar 

  • 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 5–15

    Google Scholar 

  • Ghorbel H, Jacot D (2011) Sentiment analysis of French movie reviews. In: Advances in distributed agent-based retrieval tools. Springer, Berlin

    Google Scholar 

  • Hagen M, Potthast M, Büchner M, Stein B (2015) Webis: an ensemble for Twitter sentiment detection. In: Proceedings of SEMEVAL, Denver, CO, pp 582–589

    Google Scholar 

  • 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):10–18

    Google Scholar 

  • Hatzivassiloglou V, McKeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of ACL-EACL, Madrid, pp 174–181

    Google Scholar 

  • 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 168–177

    Google Scholar 

  • Kaya M (2013) Sentiment analysis of Turkish political columns with transfer learning. PhD thesis, Middle East Technical University, Ankara

    Google Scholar 

  • 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 174–180

    Google Scholar 

  • Mao Y, Lebanon G (2006) Isotonic conditional random fields and local sentiment flow. In: Proceedings of NIPS, Vancouver, pp 961–968

    Google Scholar 

  • Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of ACL, Barcelona, pp 271–278

    Google Scholar 

  • Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135

    Google Scholar 

  • Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of EMNLP, Philadelphia, PA, pp 79–86

    Google Scholar 

  • 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 709–716

    Google Scholar 

  • Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27

    Google Scholar 

  • Rosenthal S, Ritter A, Nakov P, Stoyanov V (2014) Semeval-2014 task 9: sentiment analysis in twitter. In: Proceedings of SEMEVAL, Dublin, pp 73–80

    Google Scholar 

  • Severyn A, Moschitti A (2015) UNITN: training deep convolutional neural network for Twitter sentiment classification. In: Proceedings of SEMEVAL, Denver, CO, pp 464–469

    Google Scholar 

  • 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 1631–1642

    Google Scholar 

  • Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307

    Google Scholar 

  • 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 208–212

    Google Scholar 

  • 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):2544–2558

    Google Scholar 

  • Türkmenoğlu C, Tantuğ AC (2014) Sentiment analysis in Turkish media. Technical report, Istanbul Technical University, Istanbul

    Google Scholar 

  • Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of ACL, Philadelphia, PA, pp 417–424

    Google Scholar 

  • 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 437–445

    Google Scholar 

  • Wiebe J (2000) Learning subjective adjectives from corpora. In: Proceedings of AAAI, Austin, TX, pp 735–740

    Google Scholar 

  • Wiebe J, Wilson T, Bruce R, Bell M, Martin M (2004) Learning subjective language. Comput Linguist 30(3):277–308

    Google Scholar 

  • 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 761–769

    Google Scholar 

  • Zhao J, Liu K, Wang G (2008) Adding redundant features for CRF-based sentence sentiment classification. In: Proceedings of EMNLP, Honolulu, HI, pp 117–126

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sabancı University, Istanbul, Turkey

    Gizem Gezici & Berrin Yanıkoğlu

Authors
  1. Gizem Gezici
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Berrin Yanıkoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Berrin Yanıkoğlu .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University Qatar, Doha-Education City, Qatar

    Kemal Oflazer

  2. Electrical and Electronic Engineering, Boğaziçi University, Istanbul-Bebek, Turkey

    Murat Saraçlar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gezici, G., Yanıkoğlu, B. (2018). Sentiment Analysis in Turkish. In: Oflazer, K., Saraçlar, M. (eds) Turkish Natural Language Processing. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-90165-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90165-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90163-3

  • Online ISBN: 978-3-319-90165-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation