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Emotion Analysis of Pages in a Book to Play Background Music

Joseph, Joshma (2022) Emotion Analysis of Pages in a Book to Play Background Music. Masters thesis, Dublin, National College of Ireland.

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The approach of natural language processing, which has been extensively studied, includes recognizing emotions within texts. To make pertinent product and response recommendations to users, it may be necessary to extract human emotions or thoughts from these text. In this paper we will research on how we can analyse the emotions of pages in a book using social media data-set and play background music related to that emotion. This approach can build the mood for the chapters in the book and depict the authors emotion. With this strategy, the reader will be able to fully immerse themselves in the novel-reading experience and gain a deeper understanding of the author’s feelings through music. With the help of various Machine Learning and Deep Learning models, such as Multinomial Nave Bayes and Recurrent Neural Network-based approach, which will be applied to the word vectors produced in the feature extraction process and the padded phrases, the classification and identification of emotions are carried out in this study. In recognizing the emotions in the book’s pages, the Multinomial Naive Bayes approach had an accuracy of 62 percent and the Recurrent Neural Network methodology had an accuracy of 82 percent. The precise identification of the emotions enabled the playing of appropriate background music from a library that was specially curated with songs for various emotions.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing
B Philosophy. Psychology. Religion > Psychology > Emotions
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 26 Jan 2023 17:52
Last Modified: 02 Mar 2023 13:59

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