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.

IMDb verilerini toplayarak en popüler filmleri ve türleri analiz edip gösteren prototip bir web projesi.

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.

Classifies IMDb movie reviews as positive/negative using Logistic Regression (88.47% accuracy) and Naive Bayes models. Features TF-IDF vectorization, comprehensive text preprocessing, and a Streamlit web application for real-time sentiment prediction with detailed performance analysis and visualizations.

IMDb Reviews Classification on Plain Text Data using Word Embeddings, LSTM, GRU and Conv1D.

Sentiment Analysis of Tweets and IMDB Reviews

Supervised Learning Techniques for Sentiment Analytics

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.

A deep learning project focused on brain tumor classification using CNN and VGG16, along with sentiment analysis on drug and IMDB reviews

A machine learning pipeline to classify IMDB reviews into positive or negative sentiment using TF-IDF and Logistic Regression.

It is imdb movie reviews analysis

Javascript module for getting reviews of movie/series from imdb