Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques

lowast, M Ameen Chhajro; Khuhro, Mansoor Ahmed; Kumar, Kamlesh; Wagan, Asif Ali; Umrani, Aamir Iqbal; Laghari, Asif Ali

doi:10.17485/ijst/v13i19.230

IJST

2020

DOI: 10.17485/ijst/v13i19.230

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Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques

M Ameen Chhajro lowast¹,

Mansoor Ahmed Khuhro²,

Kamlesh Kumar³

et al.

Abstract: (2020) Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques.

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Cited by 2 publications

References 12 publications

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Network traffic classification based‐ masked language regression model using CNN

P. L.,

Emmanuel,

Rani

2024

Concurrency and Computation

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SummaryNetwork traffic classification task has become increasingly challenging. The objective behind this classification is to effectively handle bandwidth, prioritize certain types of traffic, enhance application performance, and more. In recent times, there has been a surge in exploring deep learning approaches for network traffic categorization. However, these models demand substantial volumes of training data. Additionally, many classification methods necessitate manual feature extraction, a process that is not only time‐consuming but also laborious. Addressing the challenge of identifying optimal features to enhance classification accuracy, this work introduces a deep learning model designed for effective classification of network traffic. The model comprises the following key stages: (a) The dataset involves TCP flows captured from running different network stress and web crawling tools, (b) Pre‐processing for removal of anomalies and noises using Label Encoder and OneHotEncoder, (c) The utilization of K‐BERT for feature extraction aims to retrieve local spatial–temporal features, (d) feature selection using linear regression model (LASSO) and finally, and (e) The classification of network traffic involves neural network. The model serves to enhance the precision and efficiency of the classification mission. Through comprehensive experimental analysis, it was observed that the Masked Language‐based Regression model surpassed other referenced models, achieving an exceptional accuracy of 0.97.

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Network traffic classification based‐ masked language regression model using CNN

P. L.,

Emmanuel,

Rani

2024

Concurrency and Computation

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A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language

Khan

et al. 2021

Computers

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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. Disregarding people’s opinions in Urdu and Roman Urdu and considering only resource-rich English language leads to the vital loss of this vast amount of data. Our research focused on collecting research papers related to Urdu and Roman Urdu language and analyzing them in terms of preprocessing, feature extraction, and classification techniques. This paper contains a comprehensive study of research conducted on Roman Urdu and Urdu text for a product review. This study is divided into categories, such as collection of relevant corpora, data preprocessing, feature extraction, classification platforms and approaches, limitations, and future work. The comparison was made based on evaluating different research factors, such as corpus, lexicon, and opinions. Each reviewed paper was evaluated according to some provided benchmarks and categorized accordingly. Based on results obtained and the comparisons made, we suggested some helpful steps in a future study.

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Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques

Abstract: (2020) Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques.

Cited by 2 publications

References 12 publications

Network traffic classification based‐ masked language regression model using CNN

Network traffic classification based‐ masked language regression model using CNN

A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language

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