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Semantic Sentiment Classification for COVID-19 Tweets Using Universal Sentence Encoder.

Ibrahim Eldesouky Fattoh1, Fahad Kamal Alsheref2, Waleed M Ead2

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This study developed a deep learning sentiment analysis model using Universal Sentence Encoder to analyze COVID-19 tweets. The model achieved 78.062% accuracy, outperforming traditional methods for measuring public opinion.

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Area of Science:

  • Computational linguistics
  • Public health informatics
  • Social media analytics

Background:

  • The COVID-19 pandemic saw a surge in social media data, reflecting public health status and news.
  • Analyzing public sentiment from social media is crucial for decision-makers during health crises.
  • Traditional methods for sentiment analysis often struggle with nuanced language in large datasets.

Purpose of the Study:

  • To introduce a novel deep learning sentiment analysis model for COVID-19 related tweets.
  • To evaluate the model's effectiveness in classifying sentiment (positive, neutral, negative).
  • To compare the model's performance against traditional machine learning classifiers.

Main Methods:

  • A deep learning model based on Universal Sentence Encoder was developed.
  • The dataset comprised tweets related to COVID-19, collected from Twitter.
  • The model utilized sentence embeddings to understand word sequence meaning and sentence similarity for classification.

Main Results:

  • The sentiment analysis model achieved an accuracy of 78.062%.
  • This accuracy surpassed that of traditional ML classifiers using TF-IDF.
  • Performance was also superior to a Convolutional Neural Network (CNN) classifier on the same dataset.

Conclusions:

  • The Universal Sentence Encoder-based deep learning model is effective for analyzing public sentiment on health topics like COVID-19.
  • This approach offers a more robust method for sentiment analysis compared to traditional techniques.
  • The findings highlight the potential of advanced computational models in understanding public opinion during pandemics.