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International Conference on Brain Inspired Cognitive Systems

BICS 2019: Advances in Brain Inspired Cognitive Systems pp 497–509Cite as

PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11691))

Abstract

Sentiment analysis is probably the most actively growing area of natural language processing nowadays, which leverages huge amount of user-contributed data on Internet to improve income of businesses and quality of life of consumer. The majority of existent sentiment-analysis systems is focused on English, due to lack of resources and tools for other languages. To fill this gap for Persian language, in our previous work we have compiled the first version of PerSent Persian sentiment lexicon, which was small and included only words and phrases from general domain. In this paper, we present its extension with words from three different domains and evaluate its performance on polarity classification task using various machine learning-based classifiers. We use a multi-domain dataset to evaluate the performance of our new lexicon on various domains. Our results demonstrate usefulness of the new lexicon for analysis of product and movie reviews and especially of political news in Persian language.

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Author information

Authors and Affiliations

  1. Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK

    Kia Dashtipour

  2. Department of Electrical Engineering and Computing, Rochester Institute of Technology, Dubai, UAE

    Ali Raza

  3. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

  4. Electrical and Electronic Engineering, Xi’an Jiaotong Liverpool University, Suzhou, China

    Rui Zhang

  5. School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Drive, Singapore, Singapore

    Erik Cambria

  6. School of Computing, Edinburgh Napier University, Merchiston Campus, Edinburgh, EH10 5DT, Scotland, UK

    Amir Hussain

Authors
  1. Kia Dashtipour
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  2. Ali Raza
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  3. Alexander Gelbukh
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  4. Rui Zhang
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  5. Erik Cambria
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  6. Amir Hussain
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Corresponding author

Correspondence to Kia Dashtipour .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  4. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Kaizhu Huang

  5. Northwestern Polytechnical University, Xi'an, China

    Jiangbin Zheng

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  7. Guangdong Polytechnic Normal University, Guangzhou, China

    Rongjun Chen

  8. Guangdong Polytechnic Normal University, Guangzhou, China

    Yinyin Xiao

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Dashtipour, K., Raza, A., Gelbukh, A., Zhang, R., Cambria, E., Hussain, A. (2020). PerSent 2.0: Persian Sentiment Lexicon Enriched with Domain-Specific Words. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_48

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  • DOI: https://doi.org/10.1007/978-3-030-39431-8_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39430-1

  • Online ISBN: 978-3-030-39431-8

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