The Public Pulse: A Unified NLP Framework for Sentiment, Topic, and Toxicity Analysis of YouTube Discourse

– Online platforms such as YouTube generate vast volumes of user-generated textual content that reflect public opinion, emotions, and behavioural patterns. Analysing this content is essential for understanding audience sentiment, dominant discussion themes, and the presence of toxic or harmful speech. Existing Natural Language Processing (NLP) approaches typically address sentiment analysis, topic modeling, and toxicity detection as independent tasks, results in fragmented insights and increased system complexity. This paper presents The Public Pulse, an integrated and interpretable NLP framework that unifies sentiment analysis, topic modeling, and toxicity detection within a single analytical pipeline. The system processes YouTube comments using lexicon-based sentiment analysis, Latent Dirichlet Allocation (LDA) for topic extraction, and rule-based toxicity detection, with results visualised through an interactive Streamlit dashboard. Experimental results demonstrate that the proposed approach provides coherent insights into public discourse while remaining computationally efficient and suitable for academic and resource constrained environments.