The objective of this project is to scarp the data from IMDb website and form an analysis that will help data analyst or production company to decide how they are going to proceed with making a new movie, second is to form a model to predict what are the sentiments of movies based on user reviews.
This project proposes creating emotional maps for movies by analyzing reviewers' scores and sentiments, offering a more intuitive way for moviegoers to choose films based on emotional patterns rather than just ratings or reviews.
Supervised Learning Techniques for Sentiment Analytics
IMDb Reviews Classification on Plain Text Data using Word Embeddings, LSTM, GRU and Conv1D.
Sentiment Analysis of Tweets and IMDB Reviews
This project performs sentiment analysis on IMDB movie reviews using an LSTM deep learning model. It processes text data, trains an LSTM network to classify reviews as positive or negative, and provides a prediction function for user input. The project utilizes TensorFlow, Keras, and Pandas for model building and data handling.
Javascript module for getting reviews of movie/series from imdb
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