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ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.

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Analyzing public COVID-19 sentiments using machine learning reveals predominantly negative feelings. An ensemble model combining handcrafted and automatic features achieved high accuracy, aiding policy development.

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Area of Science:

  • Computational Social Science
  • Natural Language Processing
  • Machine Learning

Background:

  • Pandemics like COVID-19 significantly impact public mental well-being, causing fear and disappointment.
  • Analyzing public sentiment from social media is crucial for developing effective mitigation policies.
  • Unstructured social media data presents challenges for accurate sentiment classification.

Purpose of the Study:

  • To develop an ensemble model combining machine learning and deep learning for accurate sentiment analysis of COVID-19 public discourse.
  • To evaluate the effectiveness of handcrafted features alongside automatic feature extraction.
  • To compare the performance of TextBlob and VADER for data annotation and various feature extraction techniques (Word2Vec, TF, TF-IDF).

Main Methods:

  • An ensemble model was developed integrating machine learning and deep learning approaches.
  • TextBlob and VADER were used for data preprocessing and annotation.
  • Machine learning models were trained using handcrafted and automatic features, including TF-IDF and Word2Vec.
  • The Extra Tree Classifier and a voting ensemble of models were evaluated.

Main Results:

  • The Extra Tree Classifier performed best with TF-IDF features and TextBlob annotation.
  • Machine learning models generally showed better performance with TF-IDF and TextBlob.
  • The proposed ensemble model achieved high accuracy (0.97 with TextBlob, 0.95 with VADER) using Word2Vec features.
  • Ensemble models with a voting criterion outperformed other machine learning models.

Conclusions:

  • Machine learning and deep learning models, particularly ensemble approaches, are effective for analyzing COVID-19 public sentiment.
  • TF-IDF features combined with TextBlob annotation provide robust results for sentiment classification.
  • Public sentiment towards COVID-19 is predominantly negative, highlighting the need for targeted support strategies.