Real-Time Sentiment Mining: Aspect-Based and Implicit Opinion Extraction in Social Media Posts

In today's digital landscape, social media platforms serve as a major source of public opinion and feedback on a wide array of topics, from products to social issues. However, extracting meaningful insights from this data is challenging due to the unstructured nature of social media text, where sentiments are often implicit and context-dependent. This study presents a novel approach for real-time aspect-based sentiment analysis (ABSA) and implicit opinion extraction tailored to social media data. Using a comprehensive dataset derived from Twitter and other popular social networks, we develop a model that identifies and categorizes specific aspects of discussion and extracts sentiments that are both explicitly stated and implied within the text. This approach leverages natural language processing techniques, including transformerbased models, to address the complexities of short, informal, and often ambiguous posts. The model is designed to operate in real time, enabling the timely detection of emerging trends and shifts in public sentiment. Experiments demonstrate that this model achieves high accuracy in recognizing sentiment-laden aspects and implicit opinions across diverse domains. The findings reveal valuable insights into user sentiments that are not overtly expressed, providing a deeper understanding of social media data for applications in business, policy, and customer feedback. Our research contributes to the growing field of ABSA by expanding sentiment analysis to include nuanced, implicit sentiment extraction, thereby enhancing real-time monitoring and decision- making processes.