Urdu News Classification: An Empirical Study Using Machine Learning Techniques

Farooq, Aman; Noreen, Zain; Batool, Safiyah; Naz, Fouzia

doi:10.1109/majicc56935.2022.9994152

Cited by 1 publication

(1 citation statement)

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“…Using an Urdu dataset comprising of many labels, emotions were classified using machine learning [41]. Research was performed to reduce the gaps in pre-processing tools in Urdu by designing a stemmer, tokenizer, and preparing a stop-words list to perform Urdu news classification [42]. Urdu text document classification was performed using DL models in comparison with ML models in product manufacturing data [43].…”

Section: Relation To Previous Workmentioning

confidence: 99%

Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

Muhammad

Burney

2023

Symmetry

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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 preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis.

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Section: Relation To Previous Workmentioning

confidence: 99%