Deep Learning-based Sentiment Analysis in Persian Language

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HEYDARI MOHAMMAD; Khazeni Mohsen; Soltanshahi Mohammad Ali

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2021 | Issue:7

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Title

Deep Learning-based Sentiment Analysis in Persian Language

Author(s)

HEYDARI MOHAMMAD | Khazeni Mohsen | Soltanshahi Mohammad Ali

Keywords

Deep learning

Natural Language Processing

Sentiment Analysis

Persian Language

Abstract

Recently, interests in the appliance of Deep learning techniques in Natural Language Processing tasks considerably increased. Sentiment Analysis is one of the most difficult tasks in Natural Language Processing, mostly in the Persian Language. Thousands of websites, blogs, social networks like Telegram, Instagram and Twitter update, and modify by Persian users around the world that contains millions of contexts. To extract knowledge of these huge amounts of raw data, Deep learning techniques became increasingly popular but there is a number of challenges that the novel models encounter with them. In this research, a hybrid Deep learning-based Sentiment Analysis model proposed and implemented on customer reviews data of Digikala Online Retailer website. We already applied the classifier based on various Deep learning networks and regularization techniques. Finally, by utilizing a hybrid approach, we achieved the best performance of 78. 3 of F1 score on three different classes: positive, negative, and neutral.

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APA: Copy

HEYDARI, MOHAMMAD, Khazeni, Mohsen, & Soltanshahi, Mohammad Ali. (2021). Deep Learning-based Sentiment Analysis in Persian Language. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949409/en

Vancouver: Copy

HEYDARI MOHAMMAD, Khazeni Mohsen, Soltanshahi Mohammad Ali. Deep Learning-based Sentiment Analysis in Persian Language. 2021. Available from: https://sid.ir/paper/949409/en

IEEE: Copy

MOHAMMAD HEYDARI, Mohsen Khazeni, and Mohammad Ali Soltanshahi, “Deep Learning-based Sentiment Analysis in Persian Language,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2021, [Online]. Available: https://sid.ir/paper/949409/en

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