PerSent 2.0: Persian sentiment lexicon enriched with domain-specific words

Kia Dashtipour; Ali Raza; Alexander Gelbukh; Rui Zhang; Erik Cambria; Amir Hussain

doi:10.1007/978-3-030-39431-8_48

PerSent 2.0: Persian sentiment lexicon enriched with domain-specific words

Kia Dashtipour^*, Ali Raza, Alexander Gelbukh, Rui Zhang, Erik Cambria, Amir Hussain

^*Corresponding author for this work

Department of Foundational Mathematics

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

15 Citations (Scopus)

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.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
Editors	Jinchang Ren, Amir Hussain, Huimin Zhao, Jun Cai, Rongjun Chen, Yinyin Xiao, Kaizhu Huang, Jiangbin Zheng
Publisher	Springer
Pages	497-509
Number of pages	13
ISBN (Print)	9783030394301
DOIs	https://doi.org/10.1007/978-3-030-39431-8_48
Publication status	Published - 2020
Event	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, China Duration: 13 Jul 2019 → 14 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11691 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
Country/Territory	China
City	Guangzhou
Period	13/07/19 → 14/07/19

Keywords

Machine learning
Persian
Sentiment analysis

Access to Document

10.1007/978-3-030-39431-8_48

Cite this

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 J. Ren, A. Hussain, H. Zhao, J. Cai, R. Chen, Y. Xiao, K. Huang, & J. Zheng (Eds.), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings (pp. 497-509). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11691 LNAI). Springer. https://doi.org/10.1007/978-3-030-39431-8_48

Dashtipour, Kia ; Raza, Ali ; Gelbukh, Alexander et al. / PerSent 2.0 : Persian sentiment lexicon enriched with domain-specific words. Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. editor / Jinchang Ren ; Amir Hussain ; Huimin Zhao ; Jun Cai ; Rongjun Chen ; Yinyin Xiao ; Kaizhu Huang ; Jiangbin Zheng. Springer, 2020. pp. 497-509 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Machine learning, Persian, Sentiment analysis",

author = "Kia Dashtipour and Ali Raza and Alexander Gelbukh and Rui Zhang and Erik Cambria and Amir Hussain",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 ; Conference date: 13-07-2019 Through 14-07-2019",

year = "2020",

doi = "10.1007/978-3-030-39431-8_48",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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 J Ren, A Hussain, H Zhao, J Cai, R Chen, Y Xiao, K Huang & J Zheng (eds), Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11691 LNAI, Springer, pp. 497-509, 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019, Guangzhou, China, 13/07/19. https://doi.org/10.1007/978-3-030-39431-8_48

PerSent 2.0: Persian sentiment lexicon enriched with domain-specific words. / Dashtipour, Kia; Raza, Ali; Gelbukh, Alexander et al.
Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. ed. / Jinchang Ren; Amir Hussain; Huimin Zhao; Jun Cai; Rongjun Chen; Yinyin Xiao; Kaizhu Huang; Jiangbin Zheng. Springer, 2020. p. 497-509 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11691 LNAI).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - PerSent 2.0

T2 - 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019

AU - Dashtipour, Kia

AU - Raza, Ali

AU - Gelbukh, Alexander

AU - Zhang, Rui

AU - Cambria, Erik

AU - Hussain, Amir

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Machine learning

KW - Persian

KW - Sentiment analysis

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DO - 10.1007/978-3-030-39431-8_48

M3 - Conference Proceeding

AN - SCOPUS:85080953387

SN - 9783030394301

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 497

EP - 509

BT - Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings

A2 - Ren, Jinchang

A2 - Hussain, Amir

A2 - Zhao, Huimin

A2 - Cai, Jun

A2 - Chen, Rongjun

A2 - Xiao, Yinyin

A2 - Huang, Kaizhu

A2 - Zheng, Jiangbin

PB - Springer

Y2 - 13 July 2019 through 14 July 2019

ER -

Dashtipour K, Raza A, Gelbukh A, Zhang R, Cambria E, Hussain A. PerSent 2.0: Persian sentiment lexicon enriched with domain-specific words. In Ren J, Hussain A, Zhao H, Cai J, Chen R, Xiao Y, Huang K, Zheng J, editors, Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings. Springer. 2020. p. 497-509. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-39431-8_48

PerSent 2.0: Persian sentiment lexicon enriched with domain-specific words

Abstract

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Keywords

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