Twitter Sentiment Analysis Using Stacking Machine Learning Approach

In recent years, sentiment analysis—also known as opinion mining—has gained popularity due to the abundance of data available on social media platforms. This analytical technique has an extensive variety of applications in different fields such as politics, customer service, social media analysis, and marketing. It can help businesses and organizations understand how their customers perceive their products or services and make informed decisions based on that information. Moreover, sentiment analysis can also aid in tracking public opinion and sentiment about specific topics or issues, which can be valuable for policymakers and social scientists. In this work, we present an evaluation of various machine learning algorithms, like Logistic Regression, Multinomial Naive Bayes, Support Vector Machine (SVM), Decision Tree, and Ensemble techniques like Gradient Boosting, Random Forest, XGBoost, AdaBoost, Weighted Averaging, and Stacking for the classification of sentiment in Twitter tweets. Our study includes visualizations such as confusion matrices and heatmaps, providing insights into the strengths and weaknesses of each algorithm. This research aims to assist practitioners and researchers in selecting the most appropriate machine learning algorithm for their particular application.