A Robust Ensemble Machine Learning Framework For Accurate Sentiment Classification Of Twitter Data

Social media platforms generate a massive amount of opinion-based data that reflects public attitudes toward various topics such as politics, products, and social events. Among these platforms, Twitter is widely used for expressing opinions in the form of short textual messages known as tweets. Analyzing these tweets can provide valuable insights into public sentiment. However, sentiment classification of Twitter data is challenging due to informal language, abbreviations, emojis, and sarcasm. This study proposes an ensemble learning framework to improve the accuracy of Twitter sentiment classification. The framework involves several stages, including data collection, preprocessing, feature extraction using techniques such as Bag-of-Words and TF-IDF, and training multiple machine learning classifiers. Ensemble methods combine the predictions of these classifiers to generate more reliable results. The performance of the proposed model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The proposed approach aims to enhance sentiment analysis performance and provide more accurate insights from social media data.