A Transformer-Driven RoBERTa Framework for Joint Sentiment and Topic Classification in Large-Scale social media Text

Abstract

“The Social Dilemma” has triggered significant online discourse, with more than 1.5 million tweets generated globally in the first month of its release and nearly 62% of them reflecting strong opinions on social media regulation. Traditional manual classification of such data is labour-intensive, errorprone, and lacks scalability, limiting the extraction of meaningful insights. To address these issues, a Natural Language Processing (NLP) pipeline is designed that preprocesses the “The Social Dilemma” Tweets dataset, followed by Exploratory Data Analysis (EDA) to identify trends, noise, and dominant language patterns. Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) with word embeddings is employed for deep contextual feature extraction with SMOTEbalanced features, enhancing semantic understanding beyond surface-level text. The extracted features are used to train and evaluate multiple Machine Learning (ML) classifiers for two parallel targets: sentiment classification and topic classification. Existing approaches such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), and Naïve Bayes Classifier (NBC) are considered for baseline performance comparison. In the proposed methodology, a Deep Neural Network (DNN) feature extractor combined with a Tao Tree Classifier (TTC) ensures higher generalization and robust classification. Hereafter, the proposed system is named “SocialTransformDeepTree.” Sentiments are categorized into Negative, Neutral, and Positive, while topics are classified into Calls for Action, Documentary Recommendation, Emotional Reactions, Irony & Self-Reflection, and Key Quotes & Insights. This hybrid framework not only improves accuracy and computational efficiency but also establishes a scalable approach for analyzing complex social media discussions surrounding documentaries with global societal impact.