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A Framework for Sentiment Analysis in Turkish: Application to Polarity Detection of Movie Reviews in Turkish

Abstract

In this work, we present a framework for unsupervised sentiment analysis in Turkish text documents. As part of our framework, we customize the SentiStrength sentiment analysis library by translating its lexicon to Turkish. We apply our framework to the problem of classifying the polarity of movie reviews. For performance evaluation, we use a large corpus of Turkish movie reviews obtained from a popular Turkish social media site. Although our framework is unsupervised, it is demonstrated to achieve a fairly good classification accuracy, approaching the performance of supervised polarity classification techniques.

Keywords

  • Sentiment Analysis
  • Input Text
  • Negative Sentiment
  • Movie Review
  • Sentiment Score

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 1

Notes

  1. 1.

    There is both a positive and a negative score because the input text may contain sentiments in both directions (e.g., “I love you, but I also hate you.”) [8].

  2. 2.

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

  3. 3.

    We do not consider the neutral class and break the ties in favor of the negative class.

  4. 4.

    Zemberek 2, http://code.google.com/p/zemberek/.

  5. 5.

    We use “_NOT_” as the keyword.

  6. 6.

    Use -explain option to obtain the sentiment scores of individual words in the text.

  7. 7.

    Beyazperde, http://www.beyazperde.com

References

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Acknowledgments

This work is supported by grant number TUBITAK-112E002, TUBITAK. We thank Umut Erogul for providing us the data.

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Correspondence to A. Gural Vural .

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Vural, A.G., Cambazoglu, B.B., Senkul, P., Tokgoz, Z.O. (2013). 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. Springer, London. https://doi.org/10.1007/978-1-4471-4594-3_45

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  • DOI: https://doi.org/10.1007/978-1-4471-4594-3_45

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