In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches

Muhammad Saeed; Naeem Ahmed; Danish Ali; Muhammad Ramzan; Muzamil Mohib; Kajol Bagga; Atif Ur Rahman; Ikram Majeed Khan

doi:10.62762/TIS.2024.585616

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Volume 1, Issue 3, IECE Transactions on Intelligent Systematics

Volume 1, Issue 3, 2024

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IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 3: 161-175

Free Access | Research Article | 09 November 2024

In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches

Muhammad Saeed 1

Naeem Ahmed 1 *

Danish Ali 2 *

Muhammad Ramzan 3

Muzamil Mohib 4

Kajol Bagga 5

Atif Ur Rahman 6

Ikram Majeed Khan 7

1 School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

2 School of Computer Science, Wuhan University, Wuhan 430070, China

3 Department of Information technology, the University of Haripur, 22620, Pakistan

4 School of Business, Nanjing University of Information Science and Technology, Nanjing 210044, China

5 Department of Computer Science, Technical Hochshule (THWS) Wuerzburg, Germany

6 Department of Computer Science, IQRA National University, Swat Campus, Pakistan

7 Coventry University, Priory St, Post code:CV1 5FB, Coventry, England,UK

* Corresponding author: Danish Ali, email: [email protected]

DOI: 10.62762/TIS.2024.585616

Received: 03 October 2024, Accepted: 27 October 2024, Published: 09 November 2024

Abstract

Sentiment analysis is the process of identifying and categorizing opinions expressed in a piece of text. It has been extensively studied for languages like English and Chinese but still needs to be explored for languages such as Urdu and Hindi. This paper presents an in-depth analysis of Urdu text using state-of-the-art supervised learning techniques and a transformer-based technique. We manually annotated and preprocessed the dataset from various Urdu blog websites to categorize the sentiments into positive, neutral, and negative classes. We utilize five machine learning classifiers: Support Vector Machine (SVM), K-nearest neighbor (KNN), Naive Bayes, Multinomial Logistic Regression (MLR), and the transformer-based multilingual BERT (mBERT) model. This model was fine-tuned to capture deep contextual embeddings specific to Urdu text. The mBERT model was pre-trained on 104 languages and optimized for Urdu-specific sentiment classification by fine-tuning it on the dataset. Our results demonstrated that the mBERT model significantly outperformed traditional classifiers, achieving an accuracy of 96.5% on the test set. The study highlights the effectiveness of transfer learning via mBERT for low-resource languages such as Urdu, making it a highly promising approach for sentiment analysis.

Graphical Abstract

In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches

Keywords

machine learning

sentiment analysis

Urdu language

natural language processing (NLP)

computational linguistics

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Cite This Article

APA Style

Saeed, M., Ahmed, N., Ali, D., Ramzan, M., Mohib, M., Bagga, K., Rahman, A. U., & Khan, I. M. (2024). In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches. IECE Transactions on Intelligent Systematics, 1(3), 161-175. https://doi.org/10.62762/TIS.2024.585616

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