Machine Learning-Based Sentiment Analysis On Twitter Data

Twitter and other social media platforms produce huge amounts of user content which displays current public emotions and feelings. The study faces difficulties because people use informal expressions and slang terms and short forms and emoji symbols and unstructured text. The research focuses on detecting emotions in Twitter data through classical machine learning methods which include Logistic Regression and Linear Support Vector Machine (SVM) and Decision Tree classifiers. The preprocessing procedures together with feature extraction methods create structured data from original tweets which can be used for classification purposes.The models use standard performance metrics which include accuracy and precision and recall and F1-score to check their ability to find emotional categories. The comparison results show that Logistic Regression and Linear SVM perform well with high-dimensional textual features while Decision Trees provide understandable classification results which make them vulnerable to data noise. The research demonstrates that traditional machine learning methods can effectively analyze social media sentiment and emotion, which helps businesses track public opinion and analyze customer feedback