[Tokenization, Topic Modeling, Sentiment Analysis, Network of Bigrams] The purpose of this project is to see if text mining techniques can ease better analysis for categorizing movies with just the Descriptions while ignoring the Genre from the dataset, IMDB_movies.csv, which is stored under the data frame variable, movies_desc. Tokenization (TF-DF) was used to increase efficiency to analyze term frequencies in movie Descriptions so that the conceptual theme of a movie franchise would be determined even if a person has never watched any of the films. Create mixtures of terms that are correlated to every topic and the mixture of topics that distinguishes each document through Topic Modeling in the dataset, IMDB_movies.csv. Sentimental Analysis focused on Movies with Sentimental Clusters that were using bing and NRC lexicons to see how Sent
