Sentiment Analysis and Research of English Literature Based on Natural Language Processing Algorithms

Abstract

A computational approach towards the analysis of emotional content in English literature, that is, sentiment analysis, is thus considered. The researchers applied this approach to a broad range of literary texts by using data preprocessing, feature engineering, and model selection to develop an accurate sentiment analysis model. With high accuracy, precision, recall, and F1-score metrics, the model thus successfully captured some of the subtle expressions of emotions in literature. Results that were offered for qualitative analysis revealed shifts in emotions, thematic variations, and stylistic features, and thus interpreted complex emotions more profoundly in literary works. Indeed, the road was bumpy with challenges including figurative language and cultural references, but the incorporation of computational techniques into traditional literary analysis has allowed rich insights into the study. This multidisciplinary approach blends computational linguistics with literature in offering an avenue toward discussing the emotional dimensions of literature from a dynamic perspective, and taking into consideration both quantitative and qualitative analyses while fostering advancements in both fields.