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The Impact of Sentiment Features on the Sentiment Polarity Classification in Persian Reviews

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Abstract

Natural language processing (NLP) techniques can prove relevant to a variety of specialties in the field of cognitive science, including sentiment analysis. This paper investigates the impact of NLP tools, various sentiment features, and sentiment lexicon generation approaches to sentiment polarity classification of internet reviews written in Persian language. For this purpose, a comprehensive Persian WordNet (FerdowsNet), with high recall and proper precision (based on Princeton WordNet), was developed. Using FerdowsNet and a generated corpus of reviews, a Persian sentiment lexicon was developed using (i) mapping to the SentiWordNet and (ii) a semi-supervised learning method, after which the results of both methods were compared. In addition to sentiment words, a set of various features were extracted and applied to the sentiment classification. Then, by employing various well-known feature selection approaches and state-of-the art machine learning methods, a sentiment classification for Persian text reviews was carried out. The obtained results demonstrate the critical role of sentiment lexicon quality in improving the quality of sentiment classification in Persian language.

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Notes

  1. Parts of Speech

  2. Objectivity score = 1—(positivity score + negativity score)

  3. A more detailed description of FarsNet and the other Persian WordNets is provided in Persian WordNet section.

  4. http://wtlab.um.ac.ir

  5. WordNet 3.1 database statistics

  6. http://dadegan.ir/catalog/farsnet

  7. http://compling.hss.ntu.edu.sg/omw/

  8. https://github.com/KevinStern/software-and-algorithms/blob/master/src/main/java/blogspot/software_and_algorithms /stern_library/optimization/HungarianAlgorithm.java

  9. point-wise mutual information

  10. If the total positive and negative sentiment polarity of a word is more than 0.5, the word is subjective.

  11. Available at https://code.google.com/archive/p/word2vec/

  12. Available at https://github.com/attardi/deepnl/

  13. www.digikala.com

  14. http://www.alexa.com/topsites/countries/IR

  15. The opinion corpus and Persian text-processing tools for non-commercial use are available on the website of Web Technology laboratory of Ferdowsi University (http://wtlab.um.ac.ir).

  16. The SVM method with different non-linear kernel functions (Sigmoid, Polynomial, RBF) was also studied that compared to the Linear SVM method is less accurate.

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Authors and Affiliations

  1. Department of Computer Engineering, Ferdowsi University of Mashhad, Azadi Sq., Mashhad, Iran

    Ehsan Asgarian & Mohsen Kahani

  2. Department of Linguistics, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran

    Shahla Sharifi

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  1. Ehsan Asgarian
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Asgarian, E., Kahani, M. & Sharifi, S. The Impact of Sentiment Features on the Sentiment Polarity Classification in Persian Reviews. Cogn Comput 10, 117–135 (2018). https://doi.org/10.1007/s12559-017-9513-1

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