Machine Learning Algorithm for Sentiment Analysis on Tweets

Since there is so much user-generated content and there is a need to derive valuable insights from it, sentiment analysis on Twitter has become a hot study topic. This study introduces a sentiment analysis machine learning system created especially for Twitter tweets. The suggested approach uses machine learning techniques to automatically categorize tweets into categories with positive, negative, or neutral sentiment. The preparation phase of the algorithm first cleans and normalizes the tweets by removing noisy elements such special characters, URLs, and hashtags. The tweets are then represented as numerical feature vectors using techniques for feature extraction. To extract the semantic and contextual data included in the tweets, a variety of lexical and syntactic features are used, such as bag-of-words, n-grams, and part-of-speech tags. Next, a supervised learning approach is utilized to train a sentiment classifier using a training dataset of labeled tweets. In order to find the best classifier for Twitter sentiment analysis, experiments are conducted with a number of well-known machine learning techniques, including Random Forest. Methods for picking the most informative features are being investigated to enhance the performance of the classifier's