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Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model.

Ahmed Alsayat1

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72388 Kingdom of Saudi Arabia.

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Summary

This study enhances sentiment analysis for pandemic-related social media data using a deep learning model with advanced word embedding and a long short-term memory (LSTM) network. The proposed ensemble model significantly improves classification accuracy over existing methods.

Keywords:
COVID-19CoronavirusData miningDeep learningEnsemble algorithmsMachine learningPandemicSentiment analysisSocial media

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Area of Science:

  • Natural Language Processing
  • Computational Social Science
  • Artificial Intelligence

Background:

  • The rapid growth of social media data, especially during the coronavirus pandemic, necessitates effective methods for understanding user opinions.
  • Traditional feature-based sentiment analysis techniques struggle with the nuances and scale of pandemic-related social media discourse.
  • Deep learning models offer enhanced representation capabilities for complex language patterns.

Purpose of the Study:

  • To improve sentiment classification performance on pandemic-related social media data.
  • To develop a customized deep learning framework incorporating advanced word embedding and a long short-term memory (LSTM) network.
  • To create a hybrid ensemble model combining the proposed LSTM classifier with other state-of-the-art sentiment analysis methods.

Main Methods:

  • Developed a deep learning model utilizing advanced word embedding for contextual understanding and a long short-term memory (LSTM) network.
  • Implemented an ensemble approach combining the baseline LSTM model with established sentiment analysis classifiers.
  • Trained and evaluated models on a custom Twitter dataset of coronavirus hashtags and public Amazon/Yelp review datasets.

Main Results:

  • The proposed word embedding and LSTM network framework effectively learns contextual word relations and handles rare words by recognizing morphological patterns (suffixes/prefixes).
  • The hybrid ensemble model demonstrated superior performance by leveraging the strengths of diverse state-of-the-art sentiment analysis techniques.
  • Statistical analysis confirmed that the developed models significantly outperformed existing methods in sentiment classification accuracy.

Conclusions:

  • The customized deep learning framework provides a robust approach for sentiment analysis in emerging situations like pandemics.
  • Ensemble modeling effectively captures diverse analytical perspectives, leading to enhanced accuracy in sentiment classification.
  • The study validates the efficacy of advanced deep learning techniques for analyzing large-scale, dynamic social media data.