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Journal articles on the topic 'Roman Urdu'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Alam, Mehreen, and Sibt Ul Hussain. "Roman-Urdu-Parl: Roman-Urdu and Urdu Parallel Corpus for Urdu Language Understanding." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (2022): 1–20. http://dx.doi.org/10.1145/3464424.

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Availability of corpora is a basic requirement for conducting research in a particular language. Unfortunately, for a morphologically rich language like Urdu, despite being used by over a 100 million people around the globe, the dearth of corpora is a major reason for the lack of attention and advancement in research. To this end, we present the first-ever large-scale publicly available Roman-Urdu parallel corpus, Roman-Urdu-Parl, with 6.37 million sentence-pairs. It is a huge corpus collected from diverse sources, annotated using crowd-sourcing techniques, and also assured for quality. It has

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Rafique, Ayesha, Kamran Malik, Zubair Nawaz, Faisal Bukhari, and Akhtar Hussain Jalbani. "Sentiment Analysis for Roman Urdu." Mehran University Research Journal of Engineering and Technology 38, no. 2 (2019): 463–70. http://dx.doi.org/10.22581/muet1982.1902.20.

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The majority of online comments/opinions are written in text-free format. Sentiment Analysis can be used as a measure to express the polarity (positive/negative) of comments/opinions. These comments/ opinions can be in different languages i.e. English, Urdu, Roman Urdu, Hindi, Arabic etc. Mostly, people have worked on the sentiment analysis of the English language. Very limited research work has been done in Urdu or Roman Urdu languages. Whereas, Hindi/Urdu is the third largest language in the world. In this paper, we focus on the sentiment analysis of comments/opinions in Roman Urdu. There is

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Ansari, Zahra, Shaukat Ali, and Fatima Khan. "USE OF ROMAN SCRIPT FOR WRITING URDU LANGUAGE." International Journal of Linguistics and Culture 1, no. 2 (2020): 165–78. http://dx.doi.org/10.52700/ijlc.v1i2.20.

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The purpose of the paper is to highlight the need of evolving a standardized Romanization table, which is used for writing Urdu language on mobile phones, laptops or tabs etc. Use of Urdu language in Roman script is in vogue in our daily life. Internet and mobile users have conveniently adopted Roman script since long for writing Urdu. But it is being used in an irregular and disorganized way. Therefore, an organized and systematic Roman script is imperative to explore ample possibilities of its usage. The current study is qualitative in nature. The Urdu Romanization table is used as a tool in

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Khan, Ihsan Ullah, Aurangzeb Khan, Wahab Khan, et al. "A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language." Computers 11, no. 1 (2021): 3. http://dx.doi.org/10.3390/computers11010003.

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Research efforts in the field of sentiment analysis have exponentially increased in the last few years due to its applicability in areas such as online product purchasing, marketing, and reputation management. Social media and online shopping sites have become a rich source of user-generated data. Manufacturing, sales, and marketing organizations are progressively turning their eyes to this source to get worldwide feedback on their activities and products. Millions of sentences in Urdu and Roman Urdu are posted daily on social sites, such as Facebook, Instagram, Snapchat, and Twitter. Disregar

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Rana, Toqir A., Kiran Shahzadi, Tauseef Rana, Ahsan Arshad, and Mohammad Tubishat. "An Unsupervised Approach for Sentiment Analysis on Social Media Short Text Classification in Roman Urdu." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (2022): 1–16. http://dx.doi.org/10.1145/3474119.

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During the last two decades, sentiment analysis, also known as opinion mining, has become one of the most explored research areas in Natural Language Processing (NLP) and data mining. Sentiment analysis focuses on the sentiments or opinions of consumers expressed over social media or different web sites. Due to exposure on the Internet, sentiment analysis has attracted vast numbers of researchers over the globe. A large amount of research has been conducted in English, Chinese, and other languages used worldwide. However, Roman Urdu has been neglected despite being the third most used language

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Noureen, Sharin Hazlin Huspi Huspi, and Zafar Ali. "Sentiment Analysis on Roman Urdu Students’ Feedback Using Enhanced Word Embedding Technique." Baghdad Science Journal 21, no. 2(SI) (2024): 0725. http://dx.doi.org/10.21123/bsj.2024.9822.

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Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study

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Khan, Lal, Ammar Amjad, Kanwar Muhammad Afaq, and Hsien-Tsung Chang. "Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media." Applied Sciences 12, no. 5 (2022): 2694. http://dx.doi.org/10.3390/app12052694.

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Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people’s perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifie

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Muhammad, Khalid Bin, and S. M. Aqil Burney. "Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets." Symmetry 15, no. 5 (2023): 1027. http://dx.doi.org/10.3390/sym15051027.

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Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the pr

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9

Rehman, Iffraah, and Tariq Rahim Soomro. "Urdu Sentiment Analysis." Applied Computer Systems 27, no. 1 (2022): 30–42. http://dx.doi.org/10.2478/acss-2022-0004.

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Abstract The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amou

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Akhter, Muhammad Pervez, Zheng Jiangbin, Irfan Raza Naqvi, Mohammed Abdelmajeed, and Muhammad Tariq Sadiq. "Automatic Detection of Offensive Language for Urdu and Roman Urdu." IEEE Access 8 (2020): 91213–26. http://dx.doi.org/10.1109/access.2020.2994950.

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Rasheed, Fahad, Mehmoon Anwar, and Imran Khan. "Detecting Cyberbullying in Roman Urdu Language Using Natural Language Processing Techniques." Pakistan Journal of Engineering and Technology 5, no. 2 (2022): 198–203. http://dx.doi.org/10.51846/vol5iss2pp198-203.

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Nowadays, social media platforms are the primary source of public communication and information. Social media platforms have become an integral part of our daily lives, and their user base is rapidly expanding as access is extended to more remote locations. Pakistan has around 71.70 million social media users that utilize Roman Urdu to communicate. With these improvements and the increasing number of users, there has been an increase in digital bullying, often known as cyberbullying. This research focuses on social media users who use Roman Urdu (Urdu language written in the English alphabet)

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Ishtiaq, Muhammad, Maryam Gulzar, and Muhammad Farhat Ullah. "RUPT: An Extension to Traditional Compilers in C++ to Support Programming in Native Language." UCP Journal of Engineering & Information Technology 3, no. 1 (2025): 41–48. https://doi.org/10.24312/ucp-jeit.03.01.591.

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The medium of instruction has a significant impact on effective communication and comprehension. The majority of literature is available in English because it is a universal language; however, if someone is provided with the information in their native language, they will comprehend the concept much more effectively. In computer science, source code of programming languages is written in the English language, whereas endemic language has its own impact. To address this gap, this study has rendered a framework, “Roman Urdu Programming Translator” (RUPT), that will be used to translate a program

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Khan, Muhammad Moin, Khurram Shahzad, and Muhammad Kamran Malik. "Hate Speech Detection in Roman Urdu." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (2021): 1–19. http://dx.doi.org/10.1145/3414524.

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Hate speech is a specific type of controversial content that is widely legislated as a crime that must be identified and blocked. However, due to the sheer volume and velocity of the Twitter data stream, hate speech detection cannot be performed manually. To address this issue, several studies have been conducted for hate speech detection in European languages, whereas little attention has been paid to low-resource South Asian languages, making the social media vulnerable for millions of users. In particular, to the best of our knowledge, no study has been conducted for hate speech detection i

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Daud, Misbah, Rafiullah Khan, Mohibullah Mohibullah, and Aitazaz Daud. "Roman Urdu Opinion Mining System (Ruomis)." Computer Science & Engineering: An International Journal 4, no. 6 (2014): 1–9. http://dx.doi.org/10.5121/cseij.2014.4601.

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Hussain, Nisar, Amna Qasim, Gull Mehak, Olga Kolesnikova, Alexander Gelbukh, and Grigori Sidorov. "Hybrid Machine Learning and Deep Learning Approaches for Insult Detection in Roman Urdu Text." AI 6, no. 2 (2025): 33. https://doi.org/10.3390/ai6020033.

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Thisstudy introduces a new model for detecting insults in Roman Urdu, filling an important gap in natural language processing (NLP) for low-resource languages. The transliterated nature of Roman Urdu also poses specific challenges from a computational linguistics perspective, including non-standardized grammar, variation in spellings for the same word, and high levels of code-mixing with English, which together make automated insult detection for Roman Urdu a highly complex problem. To address these problems, we created a large-scale dataset with 46,045 labeled comments from social media websi

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Qureshi, Muhammad Aasim, Muhammad Asif, Muhammad Farrukh Khan, Asad Kamal, and Bilal Shahid. "Roman Urdu Sentiment Analysis of Songs‘ Reviews." VFAST Transactions on Software Engineering 11, no. 1 (2023): 101–8. http://dx.doi.org/10.21015/vtse.v11i1.1399.

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To process Natural Language reviews using Machine Learning techniques is known as Sentiment Analysis. It is a way to categorize people's opinions, sentiments, and attitudes towards a specific entity. Due to easy access to the internet and smart devices, people are becoming habitual in posting reviews about any specific entity/product, they use. These reviews are very helpful for all types of users in decision-making. In the past, most of the work in Sentiment Analysis was carried out on resource-rich language but very little literature is witnessed on resource-poor languages. Very few efforts

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Li, Dun, Kanwal Ahmed, Zhiyun Zheng, et al. "Roman Urdu Sentiment Analysis Using Transfer Learning." Applied Sciences 12, no. 20 (2022): 10344. http://dx.doi.org/10.3390/app122010344.

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Numerous studies have been conducted to meet the growing need for analytic tools capable of processing increasing amounts of textual data available online, and sentiment analysis has emerged as a frontrunner in this field. Current studies are focused on the English language, while minority languages, such as Roman Urdu, are ignored because of their complex syntax and lexical varieties. In recent years, deep neural networks have become the standard in this field. The entire potential of DL models for text SA has not yet been fully explored, despite their early success. For sentiment analysis, C

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Hussain, Nisar, Amna Qasim, Gull Mehak, Olga Kolesnikova, Alexander Gelbukh, and Grigori Sidorov. "ORUD-Detect: A Comprehensive Approach to Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning–Deep Learning Models with Embedding Techniques." Information 16, no. 2 (2025): 139. https://doi.org/10.3390/info16020139.

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With the rapid expansion of social media, detecting offensive language has become critically important for healthy online interactions. This poses a considerable challenge for low-resource languages such as Roman Urdu which are widely spoken on platforms like Facebook. In this paper, we perform a comprehensive study of offensive language detection models on Roman Urdu datasets using both Machine Learning (ML) and Deep Learning (DL) approaches. We present a dataset of 89,968 Facebook comments and extensive preprocessing techniques such as TF-IDF features, Word2Vec, and fastText embeddings to ad

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Hashmi, Ehtesham, Amna Altaf, Muhammad Waqas Anwar, Muhammad Hasan Jamal, and Usama Ijaz Bajwa. "BI-SENT: bilingual aspect-based sentiment analysis of COVID-19 Tweets in Urdu language." PLOS One 20, no. 6 (2025): e0317562. https://doi.org/10.1371/journal.pone.0317562.

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The COVID-19 pandemic resulted in over 600 million cases worldwide, and significantly impacted both physical and mental health, fostering widespread anxiety and fear. Consequently, the extensive use of online social networks to express emotions made sentiment analysis a crucial tool for understanding public sentiment. Traditionally, sentiment analysis in the Urdu language has focused on sentence-level analysis. However, aspect-level sentiment analysis is increasingly important and remains underexplored due to the challenges of the costly and time-consuming manual dataset annotation process. Th

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KİŞMİR, Aykut. "The Projections of Reformist Approaches to the Genre of the Novel in Urdu Literature." Ankara Üniversitesi Dil ve Tarih-Coğrafya Fakültesi Dergisi 58, no. 2 (2018): 1347. http://dx.doi.org/10.33171/dtcfjournal.2018.58.2.9.

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Urdu edebiyatında, reform denince akla ilk olarak Sir Seyyid Ahmed Han'ın bir eğitim kurumu olarak açılmasını ön gördüğü Aligarh Kolej bünyesinde gerçekleştirilen faaliyetler gelmektedir. Sosyokültürel ve siyasi yönden, halkı içine düştüğü çıkmazdan kurtarmak isteyen aydınlar, toplumu bilinçlendirmeye yönelik çeşitli çalışmalar başlatmışlardır. Sir Seyyid Ahmet Han ve arkadaşları, Aligarh Hareketi olarak bilinen eğitim ve edebiyat faaliyetleri ile toplumun ıslahına yönelik çalışmaları hızlandırmışlardır. Bu çalışmalar, Urdu edebiyatında önemli bir farklılık yaratmıştır. Dönemin edebiyatçıları,

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Shahroz, Mobeen, Muhammad Faheem Mushtaq, Arif Mehmood, Saleem Ullah, and Gyu Sang Choi. "RUTUT: Roman Urdu to Urdu Translator Based on Character Substitution Rules and Unicode Mapping." IEEE Access 8 (2020): 189823–41. http://dx.doi.org/10.1109/access.2020.3031393.

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Bilal, Muhammad, Atif Khan, Salman Jan, Shahrulniza Musa, and Shaukat Ali. "Roman Urdu Hate Speech Detection Using Transformer-Based Model for Cyber Security Applications." Sensors 23, no. 8 (2023): 3909. http://dx.doi.org/10.3390/s23083909.

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Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and com

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Hassan, Syed Muhammad, Fayyaz Ali, Shaukat Wasi, Samreen Javeed, Imtiaz Hussain, and Syeda Nazia Ashraf. "Roman-Urdu News Headline Classification with IR Models using Machine Learning Algorithms." Indian Journal of Science and Technology 12, no. 35 (2019): 01–09. http://dx.doi.org/10.17485/ijst/2019/v12i35/146571.

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Aziz, Samia, Muhammad Shahzad Sarfraz, Muhammad Usman, Muhammad Umar Aftab, and Hafiz Tayyab Rauf. "Geo-Spatial Mapping of Hate Speech Prediction in Roman Urdu." Mathematics 11, no. 4 (2023): 969. http://dx.doi.org/10.3390/math11040969.

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Social media has transformed into a crucial channel for political expression. Twitter, especially, is a vital platform used to exchange political hate in Pakistan. Political hate speech affects the public image of politicians, targets their supporters, and hurts public sentiments. Hate speech is a controversial public speech that promotes violence toward a person or group based on specific characteristics. Although studies have been conducted to identify hate speech in European languages, Roman languages have yet to receive much attention. In this research work, we present the automatic detect

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Dewani, Amirita, Mohsin Ali Memon, Sania Bhatti, et al. "Detection of Cyberbullying Patterns in Low Resource Colloquial Roman Urdu Microtext using Natural Language Processing, Machine Learning, and Ensemble Techniques." Applied Sciences 13, no. 4 (2023): 2062. http://dx.doi.org/10.3390/app13042062.

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Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the incidents of cyberbullying and cyber hate speech. This intimidating problem has recently sought the attention of researchers and scholars worldwide. Still, the current practices to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is t

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M, Ameen Chhajro, Ahmed Khuhro Mansoor, Kumar Kamlesh, Ali Wagan Asif, Iqbal Umrani Aamir, and Ali Laghari Asif. "Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques." Indian Journal of Science and Technology 13, no. 19 (2020): 1890–900. https://doi.org/10.17485/IJST/v13i19.230.

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Abstract <strong>Objectives:</strong> This research presents multi-text classification from the news text dataset. The main purpose of this work is to classify multi-text for Urdu and Roman language using Natural Language processing and Machine Learning classification models. <strong>Methods/Statistical analysis:</strong> In this research, online news data has been collected through beautiful soup web scraping tool. In order to analyze the model accuracy news data is divided into six categories which has been composed from various online newspaper platforms. The main news corpus

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Malik, Mubasher, Hamid Ghous, Muhammad Imran Ali, Maryem Ismail, Zeeshan Haider Ali, and Hafiz Muiz Amin. "Sentiment Analysis of Roman Text: Challenges, Opportunities, and Future Directions." International Journal of Information Systems and Computer Technologies 2, no. 2 (2023): 1–16. http://dx.doi.org/10.58325/ijisct.002.02.0058.

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The rise of social media platforms has sparked significant interest among Natural Language Processing (NLP) researchers in Sentiment Analysis over the past few decades. Numerous studies have focused on analyzing the sentiment of different languages, including Urdu, which is widely spoken in South Asia and serves as the national language of Pakistan. While considerable research has been conducted on sentiment analysis using Machine Learning (ML), Deep Learning (DL), and other methods, Roman Urdu sentiment analysis holds equal importance. This paper presents a comprehensive analysis of existing

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Rajput, Vandana, and Sandeep Singh Sikarwar. "Detection of Abusive Language for YouTube Comments in Urdu and Roman Urdu using CLSTM Model." Procedia Computer Science 260 (2025): 382–89. https://doi.org/10.1016/j.procs.2025.03.214.

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Adnan Khan, Muhammad, Rizwan Ali Naqvi, Nauman Malik, Shazia Saqib, Tahir Alyas, and Dildar Hussain. "Roman Urdu News Headline Classification Empowered With Machine Learning." Computers, Materials & Continua 65, no. 2 (2020): 1221–36. http://dx.doi.org/10.32604/cmc.2020.011686.

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Mehmood, Khawar, Daryl Essam, Kamran Shafi, and Muhammad Kamran Malik. "Discriminative Feature Spamming Technique for Roman Urdu Sentiment Analysis." IEEE Access 7 (2019): 47991–8002. http://dx.doi.org/10.1109/access.2019.2908420.

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Mehmood, Khawar, Daryl Essam, Kamran Shafi, and Muhammad Kamran Malik. "Sentiment Analysis for a Resource Poor Language—Roman Urdu." ACM Transactions on Asian and Low-Resource Language Information Processing 19, no. 1 (2020): 1–15. http://dx.doi.org/10.1145/3329709.

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Ghulam, Hussain, Feng Zeng, Wenjia Li, and Yutong Xiao. "Deep Learning-Based Sentiment Analysis for Roman Urdu Text." Procedia Computer Science 147 (2019): 131–35. http://dx.doi.org/10.1016/j.procs.2019.01.202.

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Qureshi, Muhammad, Muhammad Asif, Mujahid Bashir, Hafiz Muhammad Zain, and Muhammad Shoaib. "Roman Urdu Sentiment Analysis of Reviews on PSL Anthems." Lahore Garrison University Research Journal of Computer Science and Information Technology 6, no. 03 (2022): 12–19. http://dx.doi.org/10.54692/lgurjcsit.2022.0603351.

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Due to the easy access of internet and smart devices, people are becoming habitual to give their feedback on what they hear or watch, online. These reviews are very valuable for all sorts of users. Due to the widespread online activities, the count of these reviews has raised tremendously. This fact makes it humanly impossible to analyse them manually. So it needs time that reviews to be analysed and use patterns to be found and explored through the automated channel. This led to a new field of research known as Sentiment Analysis. This paper is targeting to design a model to perform sentiment

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Ullah, Farhat, Xin Chen, Syed Bilal Hussain Shah, Saoucene Mahfoudh, Muhammad Abul Hassan, and Nagham Saeed. "A Novel Approach for Emotion Detection and Sentiment Analysis for Low Resource Urdu Language Based on CNN-LSTM." Electronics 11, no. 24 (2022): 4096. http://dx.doi.org/10.3390/electronics11244096.

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Emotion detection (ED) and sentiment analysis (SA) play a vital role in identifying an individual’s level of interest in any given field. Humans use facial expressions, voice pitch, gestures, and words to convey their emotions. Emotion detection and sentiment analysis in English and Chinese have received much attention in the last decade. Still, poor-resource languages such as Urdu have been mostly disregarded, which is the primary focus of this research. Roman Urdu should also be investigated like other languages because social media platforms are frequently used for communication. Roman Urdu

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Chandio, Bilal Ahmed, Ali Shariq Imran, Maheen Bakhtyar, Sher Muhammad Daudpota, and Junaid Baber. "Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu." Applied Sciences 12, no. 7 (2022): 3641. http://dx.doi.org/10.3390/app12073641.

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Deep neural networks have emerged as a leading approach towards handling many natural language processing (NLP) tasks. Deep networks initially conquered the problems of computer vision. However, dealing with sequential data such as text and sound was a nightmare for such networks as traditional deep networks are not reliable in preserving contextual information. This may not harm the results in the case of image processing where we do not care about the sequence, but when we consider the data collected from text for processing, such networks may trigger disastrous results. Moreover, establishi

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Zain, Muhammad, Nisar Hussain, Amna Qasim, et al. "RU-OLD: A Comprehensive Analysis of Offensive Language Detection in Roman Urdu Using Hybrid Machine Learning, Deep Learning, and Transformer Models." Algorithms 18, no. 7 (2025): 396. https://doi.org/10.3390/a18070396.

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The detection of abusive language in Roman Urdu is important for secure digital interaction. This work investigates machine learning (ML), deep learning (DL), and transformer-based methods for detecting offensive language in Roman Urdu comments collected from YouTube news channels. Extracted features use TF-IDF and Count Vectorizer for unigrams, bigrams, and trigrams. Of all the ML models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB)—the best performance was achieved by the same SVM. DL models involved evaluating Bi-LSTM and CNN models, where

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Naseeb, Amna, Muhammad Zain, Nisar Hussain, et al. "Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook." Algorithms 18, no. 6 (2025): 331. https://doi.org/10.3390/a18060331.

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Hate speech is a complex topic that transcends language, culture, and even social spheres. Recently, the spread of hate speech on social media sites like Facebook has added a new layer of complexity to the issue of online safety and content moderation. This study seeks to minimize this problem by developing an Arabic script-based tool for automatically detecting hate speech in Roman Urdu, an informal script used most commonly for South Asian digital communications. Roman Urdu is relatively complex as there are no standardized spellings, leading to syntactic variations, which increases the diff

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Qutab, Irfan, Khawar Iqbal Malik, and Hira Arooj. "Sentiment Classification Using Multinomial Logistic Regression on Roman Urdu Text." Vol 4 Issue 2 4, no. 2 (2022): 323–35. http://dx.doi.org/10.33411/ijist/2022040204.

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Sentiment analysis seeks to reveal textual knowledge of literary documents in which people communicate their thoughts and views on shared platforms, such as social blogs. On social blogs, users detail is available as short comments. A question of sentiment analysis has been raised by information across large dimensions published on these blogs. Although, some language libraries are established to address the problem of emotional analysis but limited work is available on Roman Urdu language because most of the comments or opinions available online are published in text-free style. The present s

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Siddiqui, Fatima. "A Novel Approach to Sentiment Analysis of Roman Urdu Data." Societal Transformation: AI and Big Data Journal 2, no. 1 (2024): 36–47. http://dx.doi.org/10.20547/aibd.242103.

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Waqas Azeem and Chakir Aziza. "A HYBRID FEATURE SELECTION APPROACH FOR ROMAN URDU TEXT CLASSIFICATION." Journal of Advancement in Computing 2, no. 1 (2024): 39–44. http://dx.doi.org/10.36755/jac.v2i1.61.

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Text classification is the task of assigning labels to unlabeled text data. Text classification has several applications like sentiment analysis, document classification, and fake news detection such as Machine learning (ML) methods have been used commonly in text classification in the last several years. The fundamental problem in ML is that these approaches heavily depend on feature selection methods. The models and feature selection methods used in this research. Several past types of research conclude that there is no uniform feature selection method that works well for all types of classi

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Dar, Momna, Faiza Iqbal, Rabia Latif, Ayesha Altaf, and Nor Shahida Mohd Jamail. "Policy-Based Spam Detection of Tweets Dataset." Electronics 12, no. 12 (2023): 2662. http://dx.doi.org/10.3390/electronics12122662.

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Spam communications from spam ads and social media platforms such as Facebook, Twitter, and Instagram are increasing, making spam detection more popular. Many languages are used for spam review identification, including Chinese, Urdu, Roman Urdu, English, Turkish, etc.; however, there are fewer high-quality datasets available for Urdu. This is mainly because Urdu is less extensively used on social media networks such as Twitter, making it harder to collect huge volumes of relevant data. This paper investigates policy-based Urdu tweet spam detection. This study aims to collect over 1,100,000 re

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Ali, Zeshan. "Automatic Text Summarization for Urdu Roman Language by Using Fuzzy Logic." Journal of Autonomous Intelligence 3, no. 2 (2021): 23. http://dx.doi.org/10.32629/jai.v3i2.273.

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In the new era of technology, there is the redundancy of information in the internet world, which gives a hard time for users to contain the willed outcome it, to crack this hardship we need an automated process that riddle and search the obtained facts. Text summarization is one of the normal methods to solve problems. The target of the single document epitome is to raise the possibilities of data. we have worked mostly on extractive stationed text summarization. Sentence scoring is the method usually used for extractive text summarization. In this paper, we built an Urdu Roman Language Datas

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Mehmood, Khawar, Daryl Essam, Kamran Shafi, and Muhammad Kamran Malik. "An unsupervised lexical normalization for Roman Hindi and Urdu sentiment analysis." Information Processing & Management 57, no. 6 (2020): 102368. http://dx.doi.org/10.1016/j.ipm.2020.102368.

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Masroor, Hafsa, Muhammad Saeed, Maryam Feroz, Kamran Ahsan, and Khawar Islam. "Transtech: development of a novel translator for Roman Urdu to English." Heliyon 5, no. 5 (2019): e01780. http://dx.doi.org/10.1016/j.heliyon.2019.e01780.

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Fatima, Tu Zahra Tahir Saleem A. Farhat Abdullah Muhammad Arif Khan. "Testing Matrix Language Framework Model On Urdu-English Online News Entity: A Creative Approach." Multicutural Education 7, no. 1 (2021): 265. https://doi.org/10.5281/zenodo.4460350.

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<em>Codeswitching in the context of single or multiple conversations has been a myth for language experts. Matrix language framework (MLF) model proposed by Myers Scotton (1993) has become very popular for the analysis of language pairs, and is influential in determining matrix language in different language pairs. The aim of this study is to identify matrix and embedded language in Urdu-English data sets of health and science theme. MLF model is applied to an original article on Covid-19. Data sets include language pairs from a published article on the nature of coronavirus. A qualitative des

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Zafar, Saleem Muhammad Fayaz Sajid Rahman Khattak Shams Ur Rahman Muhammad Fayaz. "Reducing Academic Burnout Through Social Support and Life Satisfaction: Evidence from Pakistan." Multicultural Education 7, no. 4 (2021): 241. https://doi.org/10.5281/zenodo.4718666.

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<em>Codeswitching in the context of single or multiple conversations has been a myth for language experts. Matrix language framework (MLF) model proposed by Myers Scotton (1993) has become very popular for the analysis of language pairs, and is influential in determining matrix language in different language pairs. The aim of this study is to identify matrix and embedded language in Urdu-English data sets of health and science theme. MLF model is applied to an original article on Covid-19. Data sets include language pairs from a published article on the nature of coronavirus. A qualitative des

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Abdulaziz, Abdullah Alzahrani Fahad Salim Al-Hafezi. "Effectiveness of Augmented Reality in Developing the Reflective Thinking Skills among Secondary School Students." Multicultural Education 7, no. 8 (2021): 678. https://doi.org/10.5281/zenodo.5294802.

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<em>Codeswitching in the context of single or multiple conversations has been a myth for language experts. Matrix language framework (MLF) model proposed by Myers Scotton (1993) has become very popular for the analysis of language pairs, and is influential in determining matrix language in different language pairs. The aim of this study is to identify matrix and embedded language in Urdu-English data sets of health and science theme. MLF model is applied to an original article on Covid-19. Data sets include language pairs from a published article on the nature of coronavirus. A qualitative des

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Chandio, Bilal, Asadullah Shaikh, Maheen Bakhtyar, et al. "Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning." Computer Modeling in Engineering & Sciences 131, no. 3 (2022): 1263–87. http://dx.doi.org/10.32604/cmes.2022.019535.

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Mehmood, Faiza, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Rehab Shahzadi, Waqar Mahmood, and Muhammad Nabeel Asim. "A Precisely Xtreme-Multi Channel Hybrid Approach for Roman Urdu Sentiment Analysis." IEEE Access 8 (2020): 192740–59. http://dx.doi.org/10.1109/access.2020.3030885.

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Mahmood, Zainab, Iqra Safder, Rao Muhammad Adeel Nawab, et al. "Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model." Information Processing & Management 57, no. 4 (2020): 102233. http://dx.doi.org/10.1016/j.ipm.2020.102233.

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