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    Author 1
    ORCID
    0000-0002-1850-5846
    Naeem Ahmed
    School of Software, Nanjing University of Information Science and Technology, Nanjing 210044
    Summary
    NAEEM AHMED is a highly motivated computer scientist hailing from Pakistan, with a strong educational background and expertise in various fields of computer science. He received the bachelor’s degree in computer science from the University of Haripur, Haripur, Pakistan, in 2019, and the M.S. degree in computer science from the University of Engineering and Technology, Taxila, in 2022. He has worked for University of Haripur as Visiting Lecturer from 2020 to 2021. He has also worked as a Lecturer in Computer Science Department, at Government Post Graduate College Haripur, Pakistan from 2021 to 2024. Currently, he is a Ph.D Scholar at Nanjing University of Information Science and Technology China. His research interests are in machine learning, information security, natural language processing, and the Internet of Things.
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    Free Access | Research Article | 09 November 2024
    In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches
    by
    Muhammad Saeed
    ,
    Naeem Ahmed
    ,
    Danish Ali
    ,
    Muhammad Ramzan
    ,
    Muzamil Mohib
    ,
    Kajol Bagga
    ,
    Atif Ur Rahman
    ,
    Ikram Majeed Khan
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 161-175, 2024 | DOI:10.62762/TIS.2024.585616
    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),... More >
    Graphical Abstract
    In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches