Sentiment Analysis of Characters Romeo and Juliet by Shakespeare Using Naïve Bayes Algorithm

Abstract

Sentiment analysis has evolved into an intriguing field of computational literary studies, merging literature and technology to gain fresh insights into classic works. This paper explores the sentiment analysis of characters in Romeo and Juliet by Shakespeare using the Naïve Bayes algorithm, aiming to understand how character sentiments shape the story’s development. In this tragic play, emotions among characters play a pivotal role, influencing the narrative’s course. However, no prior research has delved into the sentiment analysis of characters in this classic work. This research addresses this gap by providing a profound understanding of character emotions within the broader context of literary works. Moreover, it contributes to the continually evolving field of computational literary studies, emphasizing the importance and urgency of employing contemporary computational techniques to examine emotional nuances in classic literary works. Utilizing Naïve Bayes based on Bayes’ theorem and a probabilistic approach, this study achieves an accuracy rate of 81% with precision values of 82% for neutral and 79% for positive, recall scores of 96% for neutral and 73% for positive, and F1-scores 88% for neutral and 76% for positive, demonstrating the model’s effectiveness in classifying sentiments with the dataset.

Keywords: Applied Linguistic, Sentiment Analysis, Naïve Bayes, Romeo and Juliet, hakespeare.