Text Classification on Twitter Data Using Machine Learning Algorithm

Abstract

Abstract: With the exponential growth of social media platforms like Twitter, there is a need to effectively analyze and categorize the vast amount of textual data generated by users. Text classification plays a crucial role in organizing and extracting meaningful insights from this data.The proposed approach utilizes machine learning algorithms to automatically classify Twitter data into predefined categories or classes. Various machine learning techniques, including Naive Bayes, Support Vector Machines (SVM), and Random Forest, are explored to achieve accurate and efficient classification results.To evaluate the performance of the algorithms, a dataset of Twitter data is collected and preprocessed. The preprocessing steps involve tokenization, stop-word removal, stemming, and feature extraction. The extracted features are then used as input to train and test the machine learning models. Performance metrics such as precision, recall, and F1-score are used to evaluate the classification performance of each algorithm. The results indicate that the chosen machine learning algorithms achieve high accuracy in classifyingTwitter data into the predefined categories.