Abstract
This project focuses on developing a Python application to analyze and measure the similarity between movie plot summaries. The goal is to provide a tool that can assist in identifying similarities between movies based on their storyline, enabling users to discover related movies or recommend similar ones.The project utilizes natural language processing (NLP) techniques, particularly text preprocessing, vectorization, and similarity metrics, to achieve its objectives. First, it preprocesses the plot summaries by removing stop words, punctuation, and performing stemming or lemmatization to normalize the text data. Next, it employs vectorization methods such as TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings like Word2Vec or GloVe to convert the text into numerical representations suitable for similarity calculations.
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