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SentiUrdu-1M: A large-scale tweet dataset for Urdu text sentiment analysis using weakly supervised learning.

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Researchers created the first large-scale Urdu Tweet Dataset for sentiment analysis. A novel weakly supervised method, using emoticons and SentiWordNet, accurately labels tweets, outperforming existing tools like VADER and TextBlob.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Urdu, spoken by over 230 million people, is a low-resource language lacking large-scale datasets for research.
  • Deep learning and pre-trained word embeddings highlight the need for more data in under-resourced languages.
  • The scarcity of labeled data hinders meaningful studies in Urdu sentiment analysis and emotion recognition.

Purpose of the Study:

  • To address the data scarcity for Urdu language processing by creating the first large-scale Urdu Tweet Dataset.
  • To develop and evaluate a weakly supervised approach for automatic tweet labeling for sentiment analysis.
  • To compare the effectiveness of the proposed labeling method against established tools like VADER and TextBlob.

Main Methods:

  • Collected a dataset of 1,140,821 Urdu tweets.
  • Developed a weakly supervised labeling approach utilizing emoticons and SentiWordNet for automatic sentiment categorization (positive, negative, neutral).
  • Implemented baseline deep learning models to evaluate the accuracy of the proposed method against VADER and TextBlob.

Main Results:

  • The proposed weakly supervised approach demonstrated effective automatic labeling of Urdu tweets.
  • VADER and TextBlob predominantly classified tweets as neutral, showing high correlation, likely due to their inability to interpret emoticons.
  • The new dataset and labeling method provide a valuable resource for Urdu sentiment analysis and emotion recognition research.

Conclusions:

  • The creation of a large-scale Urdu Tweet Dataset is a significant contribution to low-resource language processing.
  • The proposed weakly supervised labeling method offers a viable and accurate alternative for sentiment analysis in Urdu.
  • Future research can leverage this dataset and methodology for advanced Urdu NLP tasks, including emotion recognition.