UCLA

This thesis will explore and compare Natural Language Processing methods to determine the similarity between movies based on plot descriptions. Three document embedding methods, TF-IDF, Doc2Vec, and S-BERT, are implemented. The results are evaluated using spectral clustering and normalized mutual information. Specific case studies are also presented. The objective is to identify movies that are most similar in topic or theme based solely on plot content.