Abstract:
Sentiment analysis is an essential task in natural language processing that is critical to
comprehending user attitudes and public opinion in a variety of fields. This study compares and thoroughly examines several machine-learning techniques for sentiment analysis. Linear regression, Decision Tree, Random Forest, XGBoost, KNN, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) are among the models that were examined. The main metric used in the study to assess each model's performance is accuracy. With
an accuracy of 0.9823%, (TPOT) was only behind Linear Regression, which had an
amazing accuracy of 0.9825%. Moreover, Decision Tree and Random Forest performed
admirably, with respective accuracies of 0.9762% and 0.9805%. On the other hand, the
accuracy obtained by XGBoost, KNN, and ANN were 0.9693%, 0.9753%, and 0.9783%,
in that order. Remarkably, the convolutional neural network (CNN) demonstrated a significantly reduced accuracy of 0.8199%, suggesting possible difficulties when utilizing this architecture for sentiment analysis inside the specified framework.
