A Hybrid Machine Learning Model to Predict Sentiment Analysis on X

Abstract

Social media, particularly Twitter now ????, have emerged as pivotal arenas for sentiment analysis due to their pervasive nature and significant impact on shaping opinions. Our research delves into Roman-Urdu sentiment analysis within the burgeoning realm of social media, addressing a significant gap in research. Leveraging machine learning techniques, it emphasizes the scarcity of sentiment analysis studies in this linguistic domain, specifically on platforms like Twitter. The methodology involves meticulous data collection from English and Roman-Urdu tweets, followed by comprehensive preprocessing to refine and enhance dataset quality in python. Feature extraction retrieves key characteristics like subjectivity and polarity, enabling a nuanced sentiment analysis. Our technique evaluates precision, don't forget, F1 rating, and accuracy metrics the use of a complete evaluation framework on 4 machine learning classifiers: Naïve Bayes (NB), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) algorithms. Roman Urdu sentiment analysis has advanced way to the results, which show how nicely this method works to classify all three sentiments (Hate, Offensive, and Neither) in multilingual social media content.