Advanced Sentiment Analysis for Social Media: A Mixed-Methods Approach to Context and Sarcasm Recognition

Understanding public sentiment through social media content is very important but is a challenging task due to the existence of sarcasm and context-dependent expressions.By adding context awareness and sarcasm detection, this study extends the accuracy of sentiment analysis.Qualitative content analysis was used in conjunction with quantitative sentiment scoring in a mixed-methods approach.They gathered six months' worth of social media posts and then employed sophisticated natural language processing methods -contextaware transformers.We saw a 15% improvement in the accuracy of the enhanced sentiment analysis model as well as significant improvements in precision, recall, and F1 score over traditional models.These were confirmed using regression analysis, where a positive correlation was found between contextual understanding and sentiment classification accuracy.The model sustained stable performances across six months.Most of the findings supported previous research, but a few suggested that sarcasm can be interpreted correctly when the context is strong and consistent.Further development of contextual modeling would enhance these aspects, and exploring contexts in multiple languages and mediums would help to more comprehensively understand customer sentiments.