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A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification.

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  • 1Central Department of Computer Science and Information Technology, Tribhuvan University, 44600 Kathmandu, Nepal.

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Analyzing Nepali COVID-19 tweets reveals that combining TF-IDF and FastText (hybrid features) significantly improves sentiment analysis accuracy. This hybrid approach outperforms individual methods and state-of-the-art techniques for understanding public sentiment during the pandemic.

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Area of Science:

  • Natural Language Processing
  • Computational Social Science
  • Public Health

Background:

  • COVID-19 has caused widespread mortality, with mental health impacts amplified by public fear.
  • Social media, particularly Twitter, serves as a crucial platform for expressing public sentiment regarding health crises.
  • Sentiment analysis of social media data requires sophisticated methods to interpret both linguistic structure and meaning.

Purpose of the Study:

  • To analyze public sentiment towards COVID-19 in the Nepali language using social media data.
  • To evaluate the effectiveness of combined syntactical and semantic text representation methods for sentiment analysis.
  • To compare the performance of multiple machine learning classifiers using different feature extraction techniques.

Main Methods:

  • Utilized TF-IDF and FastText for text representation, combining them into hybrid features.
  • Implemented nine machine learning classifiers: Logistic Regression, SVM, Naive Bayes, KNN, Decision Trees, Random Forest, Extreme Tree, AdaBoost, and MLP.
  • Evaluated methods on the publicly available Nepali-COVID-19 tweets dataset (NepCov19Tweets) with Positive, Negative, and Neutral categories.

Main Results:

  • The hybrid feature extraction method demonstrated superior performance compared to TF-IDF and FastText alone.
  • Machine learning classifiers achieved higher accuracy when using hybrid features across all tested algorithms.
  • The proposed hybrid approach achieved excellent performance, surpassing existing state-of-the-art methods.

Conclusions:

  • Combining syntactical (TF-IDF) and semantic (FastText) features offers a robust approach for COVID-19 sentiment analysis in Nepali.
  • The hybrid feature method enhances the accuracy of sentiment classification on social media data.
  • This study provides valuable insights into public sentiment during the pandemic, applicable to public health communication and policy.