Leveraging Ensemble Learning for Enhanced Sentiment Analysis

Sentiment analysis assesses emotions in textual data, vital in applications like product reviews and social media. Ensemble learning enhances sentiment analysis by integrating diverse algorithms, improving predictive accuracy and robustness in interpretation. This work meets the growing demand for robust sentiment interpretation tools by combining ensemble learning with sentiment analysis. Ensemble methods integrate diverse learning algorithms, crucial in assessing textual emotions, particularly in areas like product reviews and social media within natural language processing (NLP). Our contribution involves developing tailored artificial neural network (ANN) sentiment classifiers proficient in capturing intricate language sequences for quality reviews. Leveraging the IMDB movie reviews dataset, our approach demonstrates effectiveness through various evaluation metrics. Utilizing k-nearest neighbour (KNN), Random Forest, and Naive Bayes algorithms, along with k-means clustering and regressive learning, we employ ensemble learning to analyze sentiments in online reviews. This effectively addresses the resource-intensive retraining process in conventional sentiment analysis methods, resulting in accurate outcomes for emotional AI.