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🎬 This repository houses a very simple movie review site, with "Get Out" as the showcased example. It includes features for posting reviews, rating movies, and user interactions, providing a straightforward yet functional platform for movie enthusiasts to share their opinions and insights on films.
Movie Rate Prediction with Tensorflow
Supervised Learning
This ml model classifies the movie reviews entered by the audience into positive or negative opinions.
Contains implemented Machine learning and deep learning code and description.
Movie Review Sentiment Classification with Naive Bayes Classifier with BOW features implemented only using Python standard libraries
Build custom vacab, Ham /Spam using tfidf , Movie review classification using TFIDF
Movie Review sentiment analysis using imdb review dataset with Sklearn library.
Movie Review sentiment classifier from IMDB reviews using RNN (using pre-trained GloVe word embeddings and LSTM) with about 88.5% accuracy.
Classifies the movie reviews as positive or negative using LSTM Networks
Works done during Fall'19 term at UNB in DeepLearning field for Adv Remote Sensing course
A neural network model for sentiment analysis of movie reviews using IMDb dataset. The model is built using PyTorch and BERT as the feature extractor.
Movie Review Sentiment Analysis | Built in Flask and Deployed to Docker
Movie Review Classifier using Natural Language Processing (Python)
This repo contains text feature extraction & text classification codes on real world dataset
Binary Sentiment Analysis model for classification of movie reviews
classifying movie reviews as positive or negative
Movie Review Classification with TF-IDF Vectorize & SVC given a set of text movie reviews that have been labeled negative or positive