There are 3 repositories under movie-reviews topic.
Movie Ratings Synchronization with Python
Watch movies with the freedom (not) to filter
Data pipeline performing ETL to AWS Redshift using Spark, orchestrated with Apache Airflow
A Simple IMDb API for Python
Naver movie review sentiment classification with KoBERT
Deep-Atrous-CNN-Text-Network: End-to-end word level model for sentiment analysis and other text classifications
Write reviews, add video streaming, or create a Movies, Anime and TV Shows database with MovieWP WordPress theme.
Show metacritic metascore and user ratings on Bandcamp, Apple Itunes, Amazon, IMDb, Google Play, TV.com, Steam, Gamespot, Rotten Tomatoes, Serienjunkies, BoxOfficeMojo, allmovie.com, movie.com, Wikipedia, themoviedb.org, letterboxd, TVmaze, TVGuide, followshows.com, TheTVDB, ConsequenceOfSound, Pitchfork, Last.fm, TVNfo, rateyourmusic.com, GOG, Epic Games Store
A cli tool to get movie ratings and reviews directly to your terminal!
Dictionaries for sentiment analysis
PyTorch implementation of Sentiment Analysis of the long texts written in Serbian language (which is underused language) using pretrained Multilingual RoBERTa based model (XLM-R) on the small dataset.
Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets
Retrofit has been Handled !! || Consumable code for request Public API (TMDb API) || :books: :movie_camera:
Sentiment Analysis of movie reviews by sklearn's naive bayes and TfIdf word vectorizer.
🍿 Welcome to The MovieVerse: Your definitive gateway to cinema! Explore, discover, and immerse yourself in a vast collection of films with our web platform. Experience the world of movies on our web version today. Exciting news: a mobile app is in the works for both iOS and Android. Show your love for cinema by giving us a star! 🌟
Build a Movie Reviews Sentiment Classifier with Google's BERT Language Model
Show Letterboxd rating on imdb.com, metacritic.com, rottentomatoes.com, BoxOfficeMojo, Amazon, Google Play, allmovie.com, Wikipedia, themoviedb.org, movies.com
A rating-based sentiment dataset of IMDB movie reviews (WASSA 2014)
While Deep Learning is a subset of Machine Learning, the prediction methodology in deep learning is different and works similar to how a human brain uses neural pathways to process information & learn from it. In this workshop we will learn about the building blocks of deep learning, neural networks, and how they work. We'll start with Logistic Regression - a simple and basic neural network classification algorithm, having just a one-layer neural network. These are the resources for the first session of Your Path to Deep Learning.
A Material-Designed light reader for better experience on reading news, movie reviews and trending articles [ 一款 MD 风格的极轻阅读 App,提供知乎日报、豆瓣电影等资源 ]
Labeled sentences from IMDb movie reviews
🎬 Python IMDB client using the IMDB JSON web service made available for their iOS application.
I have built a model which will predict the sentiment of movie reviews. I used Naive-Bayes, SVM, and RNN+LSTM based model to obtain really good result.
Movie info app built with OMDb API that gets movie info from IMDb and Rotten Tomatoes.
Python Data Analytics, Machine Learning & Natural Language Processing
Recurrent Neural Network to classify the sentiments of the IMDb Movie Review.
This is my portfolio, which includes projects on Django, such as Movie Finder, Sentimento (Sentiment Analysis App) or To-Do App, as well as projects on JavaScript and links to my other works
Popcorntime Wordpress Theme
This is the repository for the Sentiment Analysis task on IMDB Movie Reviews for classifying positive and negative reviews, using LSTM networks, TensorFlow and GloVe embedding.
네이버 영화 164397건 중 140자 평이 있는 영화별 평점 raw data for spark
Roger Ebert's movie ratings prediction
In this project, I used Bert transformer for analyzing movie reviews.
🎬 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.