Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data using GPT

Aim:

          The aim of this research is to improve the accuracy and contextual understanding of sentiment analysis in COVID-19-related tweets by combining the rule-based approach of TextBlob with the deep learning capabilities of GPT-2.

Abstract:

         The prevalence of discussions on social media platforms, especially Twitter, regarding COVID-19 has spurred the need for effective sentiment analysis to comprehend public opinions and sentiments. In this proposed system, we leverage two powerful natural language processing tools—TextBlob and GPT-2—to enhance the accuracy of sentiment prediction in COVID-19-related tweets. TextBlob, a versatile Python library, is employed for its simplicity and efficiency in extracting sentiment from textual data. It serves as the initial sentiment analysis component, providing a baseline for sentiment classification. Additionally, we harness the capabilities of GPT-2, a state-of-the-art language model, to predict sentiment and capture nuanced contextual information from the tweets. Our methodology involves preprocessing the Twitter data and applying TextBlob for sentiment analysis. Subsequently, the GPT-2 model is fine-tuned on the same dataset to generate more context-aware predictions. By integrating these two approaches, we aim to enhance the overall accuracy and depth of sentiment analysis, considering both explicit and implicit sentiments present in the text.

         The effectiveness of the proposed system will be evaluated using a dataset of COVID-19-related tweets. Comparative analysis with traditional sentiment analysis methods will be conducted to assess the improvement achieved through the combination of TextBlob and GPT-2. This system not only contributes to the field of sentiment analysis but also offers insights into the potential of combining rule-based and deep learning approaches for enhanced sentiment prediction. The envisioned outcome of this research is a robust sentiment analysis system that can provide more nuanced insights into the diverse sentiments expressed in COVID-19-related discussions on Twitter. The integration of TextBlob and GPT-2 offers a promising avenue for advancing sentiment analysis techniques, paving the way for more accurate and contextually aware assessments of public sentiment in the evolving landscape of the pandemic.

Proposed Method:

          The proposed method combines two powerful natural language processing tools, TextBlob and GPT-2, to enhance sentiment analysis of COVID-19 tweets. TextBlob serves as a rule-based approach for initial sentiment extraction, while GPT-2, a deep learning model, is fine-tuned on the same dataset for context-aware predictions. The integration aims to improve accuracy and capture nuanced sentiments. The research evaluates the system’s effectiveness using a COVID-19 tweet dataset, comparing it with traditional sentiment analysis methods and contributing to a more nuanced understanding of sentiments expressed in pandemic-related discussions on Twitter.

Advantages:

Combined Strengths: Integrating TextBlob and GPT-2 leverages the rule-based simplicity of TextBlob and the contextual understanding of GPT-2 for a more comprehensive sentiment analysis.

Nuanced Insights: The system aims to provide more nuanced insights into the diverse sentiments expressed in COVID-19-related discussions by capturing both explicit and contextual sentiments.

Context-Aware Predictions: GPT-2’s fine-tuning on the Twitter dataset enables the model to generate context-aware predictions, improving the overall accuracy of sentiment analysis.