Application of Machine Learning Algorithms for Analyzing Sentiments Using Twitter Dataset

Sentiment analysis is a Natural Language Processing (NLP) approach used to determine whether a piece of text is negative, positive, or neutral based on the emotions it expresses. Despite advancements, several challenges persist in this field, including handling sarcasm, context ambiguity, and the presence of noisy data. Recent techniques, such as deep learning models and transformer-based approaches, have improved accuracy but still face limitations in computational efficiency and real-time analysis. This article employs Machine Learning (ML) techniques to analyze text and determine the sentiments of reviews shared on Twitter, aiming to address these challenges. The proposed system extracts attributes from the text, such as the sentiment polarity (whether the sentiment is negative or positive) and the topic or theme of the discussion. A comparison with prior work on sentiment analysis in tweets is presented, along with a comprehensive study of feature extraction and representation methods. The study evaluates the performance of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, based on different feature extraction techniques. Using the Bag of Words technique, SVM achieved the highest accuracy of 94.58%, while using term frequency, Random Forest showed the highest accuracy of 94.99%. The developed algorithms for sentiment analysis in tweets are useful for analyzing social feedback, product surveys, and social media comments. Combining neural networks and deep learning improves the accuracy of sentiment analysis and provides opportunities to analyze various types of emotions, enabling proactive responses to public sentiment.