Comparative Analysis of Machine Learning Techniques for Opinion Mining in Web Texts Using Artificial Intelligence

Abstract

This paper presents a comparative analysis of machine learning techniques for opinion mining in web texts using artificial intelligence. Opinion mining, also known as sentiment analysis, involves extracting and classifying opinions from textual data found on the web, such as reviews and blogs. The study evaluates the effectiveness of traditional data mining classifiers Naïve Bayes, k-Nearest Neighbor, and Random Forest and neural network classifiers LVQ, Elman, and FFNN. A novel approach combining Naïve Bayes and SVM in an ensemble method is proposed to enhance classification accuracy. The KINN algorithm, a neural network-based model, is introduced, demonstrating improved performance over existing methods. Experimental results using a dataset from Kaggle show that the proposed methods achieve higher accuracy in sentiment classification, offering valuable insights for improving product and service quality based on customer feedback.