Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets

Ali Nazarizadeh; Touraj Banirostam; Minoo Sayyadpour

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arXiv · 2212.06041

Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets

Ali Nazarizadeh·Touraj Banirostam·Minoo Sayyadpour

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Abstract

Sentiment analysis aims to extract people's emotions and opinion from their comments on the web. It widely used in businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Most of articles in this area have concentrated on the English language whereas there are limited resources for Persian language. In this review paper, recent published articles between 2018 and 2022 in sentiment analysis in Persian Language have been collected and their methods, approach and dataset will be explained and analyzed. Almost all the methods used to solve sentiment analysis are machine learning and deep learning. The purpose of this paper is to examine 40 different approach sentiment analysis in the Persian Language, analysis datasets along with the accuracy of the algorithms applied to them and also review strengths and weaknesses of each. Among all the methods, transformers such as BERT and RNN Neural Networks such as LSTM and Bi-LSTM have achieved higher accuracy in the sentiment analysis. In addition to the methods and approaches, the datasets reviewed are listed between 2018 and 2022 and information about each dataset and its details are provided.

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Ali Nazarizadeh, Touraj Banirostam, Minoo Sayyadpour. 2022-11-09. Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets. https://arxiv.org/abs/2212.06041

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