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"Urdu language"
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Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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 classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%.
Journal Article
Urdu language processing: a survey
Extensive work has been done on different activities of natural language processing for Western languages as compared to its Eastern counterparts particularly South Asian Languages. Western languages are termed as resource-rich languages. Core linguistic resources e.g. corpora, WordNet, dictionaries, gazetteers and associated tools being developed for Western languages are customarily available. Most South Asian Languages are low resource languages e.g. Urdu is a South Asian Language, which is among the widely spoken languages of sub-continent. Due to resources scarcity not enough work has been conducted for Urdu. The core objective of this paper is to present a survey regarding different linguistic resources that exist for Urdu language processing, to highlight different tasks in Urdu language processing and to discuss different state of the art available techniques. Conclusively, this paper attempts to describe in detail the recent increase in interest and progress made in Urdu language processing research. Initially, the available datasets for Urdu language are discussed. Characteristic, resource sharing between Hindi and Urdu, orthography, and morphology of Urdu language are provided. The aspects of the pre-processing activities such as stop words removal, Diacritics removal, Normalization and Stemming are illustrated. A review of state of the art research for the tasks such as Tokenization, Sentence Boundary Detection, Part of Speech tagging, Named Entity Recognition, Parsing and development of WordNet tasks are discussed. In addition, impact of ULP on application areas, such as, Information Retrieval, Classification and plagiarism detection is investigated. Finally, open issues and future directions for this new and dynamic area of research are provided. The goal of this paper is to organize the ULP work in a way that it can provide a platform for ULP research activities in future.
Journal Article
RUSAS: Roman Urdu Sentiment Analysis System
Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify the sentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internet using various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer to communicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect. Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limited linguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompasses extracting subjective expressions in Roman Urdu and determining the implied opinionated text polarity. The primary sources of the dataset are Daraz (an e-commerce platform), Google Maps, and the manual effort. The contributions of this study include a Bilingual Roman Urdu Language Detector (BRULD) and a Roman Urdu Spelling Checker (RUSC). These integrated modules accept the user input, detect the text language, correct the spellings, categorize the sentiments, and return the input sentence’s orientation with a sentiment intensity score. The developed system gains strength with each input experience gradually. The results show that the language detector gives an accuracy of 97.1% on a close domain dataset, with an overall sentiment classification accuracy of 94.3%.
Journal Article
Urdu practical dictionary : Urdu-English/English-Urdu
Provides four thousand entries, covers the basic of Urdu grammar, and includes practical cultural information.
Book
Roman Urdu Sentiment Analysis Using Transfer Learning
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, CNN has surpassed in accuracy, although it still has some imperfections. To begin, CNNs need a significant amount of data to train. Second, it presumes that all words have the same impact on the polarity of a statement. To fill these voids, this study proposes a CNN with an attention mechanism and transfer learning to improve SA performance. Compared to state-of-the-art methods, our proposed model appears to have achieved greater classification accuracy in experiments.
Journal Article
Detection of Cyberbullying Patterns in Low Resource Colloquial Roman Urdu Microtext using Natural Language Processing, Machine Learning, and Ensemble Techniques
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 the recent prevalence of regional languages in social media, the dearth of language resources, and flexible detection approaches, specifically for low-resource languages. In this context, most existing studies are oriented towards traditional resource-rich languages and highlight a huge gap in recently embraced resource-poor languages. One such language currently adopted worldwide and more typically by South Asian users for textual communication on social networks is Roman Urdu. It is derived from Urdu and written using a Left-to-Right pattern and Roman scripting. This language elicits numerous computational challenges while performing natural language preprocessing tasks due to its inflections, derivations, lexical variations, and morphological richness. To alleviate this problem, this research proposes a cyberbullying detection approach for analyzing textual data in the Roman Urdu language based on advanced preprocessing methods, voting-based ensemble techniques, and machine learning algorithms. The study has extracted a vast number of features, including statistical features, word N-Grams, combined n-grams, and BOW model with TFIDF weighting in different experimental settings using GridSearchCV and cross-validation techniques. The detection approach has been designed to tackle users’ textual input by considering user-specific writing styles on social media in a colloquial and non-standard form. The experimental results show that SVM with embedded hybrid N-gram features produced the highest average accuracy of around 83%. Among the ensemble voting-based techniques, XGboost achieved the optimal accuracy of 79%. Both implicit and explicit Roman Urdu instances were evaluated, and the categorization of severity based on prediction probabilities was performed. Time complexity is also analyzed in terms of execution time, indicating that LR, using different parameters and feature combinations, is the fastest algorithm. The results are promising with respect to standard assessment metrics and indicate the feasibility of the proposed approach in cyberbullying detection for the Roman Urdu language.
Journal Article