A Novel Social Media-Based Adaptable Approach for Sentiment Analysis Data

Due to its interactive and real-time character, gathering public opinion through the analysis of massive social data has received considerable interest. Recent research has used sentiment analysis and social media to do this to follow major events by monitoring people's behaviour. In this article, we provide a flexible approach to sentiment analysis that instantly pulls user opinions from social media postings and evaluates them. The suggested method entails first creating a dynamic dictionary of words' polarity based on a chosen collection of hashtags associated with a certain topic, then categorizing the tweets into many classifications by adding additional characteristics that sharply fine-tune the polarity degree of a post. We categorized the tweets on the 2022 French presidential election to verify our methodology. The prototype tests' findings demonstrated high accuracy in identifying classes as well classes' subclasses.