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Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries.
D Sunitha1, Raj Kumar Patra2, N V Babu3
1Department of Computer Science & Engineering, Kamala Institute of Technology & Science, Singapur, Telangana 505468, India.
Pattern Recognition Letters
|April 25, 2022
Summary
Analyzing real-time tweets about coronavirus using a novel sentiment analysis model helps governments monitor public mood. This approach achieved high accuracy in classifying emotions like anger, sadness, joy, and fear from Indian and European users.
Area of Science:
- Computational Social Science
- Natural Language Processing
- Public Health Informatics
Background:
- The COVID-19 pandemic has caused widespread societal impact, necessitating effective monitoring strategies.
- Social media platforms, particularly Twitter, offer valuable real-time insights into public sentiment during health crises.
- Understanding public sentiment is crucial for governmental response and public health initiatives.
Purpose of the Study:
- To develop and evaluate a sentiment analysis model for real-time Twitter data related to the coronavirus pandemic.
- To assess the model's effectiveness in classifying user sentiments (anger, sadness, joy, fear) from Indian and European populations.
- To leverage social media analytics for improved crisis management and public health awareness.
Main Methods:
- Collection of approximately 3100 real-time tweets from Indian and European users between March 2020 and November 2021.
- Data preprocessing and exploratory analysis for data understanding.
- Feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), GloVe, Word2Vec, and fastText embeddings.
- Application of an ensemble classifier combining Gated Recurrent Unit (GRU) and Capsule Neural Network (CapsNet) for sentiment classification.
Main Results:
- The proposed sentiment analysis model demonstrated high prediction accuracy.
- Achieved 97.28% accuracy for classifying Indian users' sentiments.
- Achieved 95.20% accuracy for classifying European users' sentiments.
Conclusions:
- The developed sentiment analysis model is effective for analyzing public emotions expressed on Twitter regarding the coronavirus.
- Real-time sentiment analysis of social media data can provide actionable insights for public health authorities.
- The model's high accuracy highlights the potential of advanced machine learning techniques in crisis monitoring and management.
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