Skip to main content
SpringerLink
Log in

A Web Search Enhanced Feature Extraction Method for Aspect-Based Sentiment Analysis for Turkish Informal Texts

  • Conference paper
  • First Online:
Big Data Analytics and Knowledge Discovery (DaWaK 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9829))

Included in the following conference series:

Abstract

In this article, a new unsupervised feature extraction method for aspect-based sentiment analysis is proposed. This method improves the performance of frequency based feature extraction by using an online search engine. Although frequency based feature extraction methods produce good precision and recall values on formal texts, they are not very successful on informal texts. Our proposed algorithm takes the features of items suggested by frequency based feature extraction methods, then, eliminates the features which do not co-occur with the item, whose features are sought, on the Web. Since the proposed method constructs the candidate feature set of the item from the Web, it is domain-independent. The results of experiments reveal that for informal Turkish texts, much higher performance than frequency based method is achieved.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Notes

  1. 1.

    https://tech.yandex.com/xml/.

  2. 2.

    http://www.donanimhaber.com.

  3. 3.

    http://jsoup.org/.

  4. 4.

    https://www.mysql.com.

  5. 5.

    http://www.donanimhaber.com.

  6. 6.

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

References

  1. Akın, A.A., Akın, M.D.: Zemberek, an open source NLP framework for Turkic languages. Structure 10, 1–5 (2007)

    Google Scholar 

  2. Allen, K.G., Reynolds, T.: Thanksgiving weekend shopping brings big in-store and online crowds, according to NRF survey (2015). https://nrf.com/media/press-releases/thanksgiving-weekend-shopping-brings-big-store-and-online-crowds-according-nrf/. Accessed 29 November 2015

  3. Aronoff, M., Fudeman, K.: Thinking about morphology and morphological analysis. In: What is Morphology?, pp. 1–31. Blackwell Publishing (2004)

    Google Scholar 

  4. Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reynar, J.: Building a sentiment summarizer for local service reviews. In: WWW Workshop on NLP in the Information Explosion Era, vol. 14, pp. 339–348 (2008)

    Google Scholar 

  5. Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 804–812. Association for Computational Linguistics (2010)

    Google Scholar 

  6. Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)

    Google Scholar 

  7. Hai, Z., Chang, K., Kim, J.: Implicit feature identification via co-occurrence association rule mining. In: Gelbukh, A.F. (ed.) CICLing 2011, Part I. LNCS, vol. 6608, pp. 393–404. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  8. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)

    Google Scholar 

  9. Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1035–1045. Association for Computational Linguistics (2010)

    Google Scholar 

  10. Jin, W., Ho, H.H., Srihari, R.K.: A novel lexicalized hmm-based learning framework for web opinion mining. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 465–472. Citeseer (2009)

    Google Scholar 

  11. Li, F., Han, C., Huang, M., Zhu, X., Xia, Y.-J., Zhang, S., Yu, H.: Structure-aware review mining and summarization. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 653–661. Association for Computational Linguistics (2010)

    Google Scholar 

  12. Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 375–384. ACM (2009)

    Google Scholar 

  13. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  14. Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)

    Google Scholar 

  15. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  16. Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Kao, A., Poteet, S.R. (eds.) Natural Language Processing and Text Mining, pp. 9–28. Springer, London (2007)

    Chapter  Google Scholar 

  17. Schouten, K., Frasincar, F.: Finding implicit features in consumer reviews for sentiment analysis. In: Casteleyn, S., Rossi, G., Winckler, M. (eds.) ICWE 2014. LNCS, vol. 8541, pp. 130–144. Springer, Heidelberg (2014)

    Google Scholar 

  18. Somasundaran, S., Wiebe, J.: Recognizing stances in online debates. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 1, pp. 226–234. Association for Computational Linguistics (2009)

    Google Scholar 

  19. Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden sentiment association in chinese web opinion mining. In: Proceedings of the 17th International Conference on World Wide Web, pp. 959–968. ACM (2008)

    Google Scholar 

  20. Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)

    Google Scholar 

  21. Vural, A.G., Cambazoglu, B.B., Senkul, P., Tokgoz, Z.O.: A framework for sentiment analysis in Turkish: application to polarity detection of movie reviews in Turkish. In: Gelenbe, E., Lent, R. (eds.) Computer and Information Sciences III, pp. 437–445. Springer, London (2013)

    Chapter  Google Scholar 

  22. Yu, J., Zha, Z.-J., Wang, M., Chua, T.-S.: Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1496–1505. Association for Computational Linguistics (2011)

    Google Scholar 

Download references

Acknowledgements

This work is supported by Ministry of Science, Technology and Industry with funding Project No. 0740.STZ.2014.

Author information

Authors and Affiliations

  1. Computer Engineering Department, Middle East Technical University, Ankara, Turkey

    Batuhan Kama, Murat Ozturk, Pinar Karagoz & Ismail Hakki Toroslu

  2. Huawei Turkey R&D Center, Istanbul, Turkey

    Ozcan Ozay

Authors
  1. Batuhan Kama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Murat Ozturk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pinar Karagoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ismail Hakki Toroslu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ozcan Ozay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pinar Karagoz .

Editor information

Editors and Affiliations

  1. University of Science and Technology , Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University , Osaka, Japan

    Takahiro Hara

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Kama, B., Ozturk, M., Karagoz, P., Toroslu, I.H., Ozay, O. (2016). A Web Search Enhanced Feature Extraction Method for Aspect-Based Sentiment Analysis for Turkish Informal Texts. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science(), vol 9829. Springer, Cham. https://doi.org/10.1007/978-3-319-43946-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43946-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43945-7

  • Online ISBN: 978-3-319-43946-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation