Social media platforms have evolved into primary channels for public discourse, where users express opinions on brands, events, and socio-political issues. Traditional sentiment analysis systems focus predominantly on emotion classification while lacking transparency, authenticity verification, and contextual grounding. This paper presents Sent-X, a novel framework that integrates machine learning-based sentiment classification, explainable artificial intelligence (XAI), automated content au- thenticity assessment, and web-based contextual exploration. The proposed system addresses three critical limitations of existing approaches: (1) lack of interpretability in predictions,

(2) susceptibility to bot-driven manipulation, and (3) absence of real-world context linking. Experimental evaluation on Twitter datasets demonstrates that Sent-X improves interpretability by 100% through feature-level explanations, enhances contextual awareness by over 90% via automated keyword-to-event map- ping, and reduces sentiment distortion from automated influence by approximately 60%. The modular architecture ensures scalability and maintainability while preserving computational efficiency. Results indicate that Sent-X provides more reliable and trustworthy insights for social media analytics compared to conventional approaches.

Index Terms—Sentiment Analysis, Explainable AI, Social Media Analytics, Bot Detection, Context-Aware Systems, NLP, Authenticity Verification