Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Twitter Sentiment Analysis | 1,322 | a year ago | 20 | mit | Python | |||||
Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc. | ||||||||||
Text Classification Pytorch | 538 | 5 years ago | 4 | mit | Python | |||||
Text classification using deep learning models in Pytorch | ||||||||||
Doc Han Att | 193 | 7 years ago | 5 | Jupyter Notebook | ||||||
Hierarchical Attention Networks for Chinese Sentiment Classification | ||||||||||
Twitter Sentiment Cnn | 133 | 6 years ago | 5 | Python | ||||||
An implementation in TensorFlow of a convolutional neural network (CNN) to perform sentiment classification on tweets. | ||||||||||
Newsmtsc | 122 | 3 months ago | 26 | March 14, 2022 | 3 | other | Python | |||
Target-dependent sentiment classification in news articles reporting on political events. Includes a high-quality data set of over 11k sentences and a state-of-the-art classification model. | ||||||||||
Context | 116 | 5 years ago | 1 | gpl-3.0 | C++ | |||||
ConText v4: Neural networks for text categorization | ||||||||||
Cnn Yelp Challenge 2016 Sentiment Classification | 104 | 4 years ago | 3 | Jupyter Notebook | ||||||
IPython Notebook for training a word-level Convolutional Neural Network model for sentiment classification task on Yelp-Challenge-2016 review dataset. | ||||||||||
Cnn Text Classification | 101 | 3 years ago | Jupyter Notebook | |||||||
Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0 | ||||||||||
Sentiment | 85 | a year ago | 8 | March 31, 2023 | 4 | mit | PHP | |||
An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website. | ||||||||||
Twitter Sentiment Analysis Classical Approach Vs Deep Learning | 70 | 3 years ago | Jupyter Notebook | |||||||
This project's aim, is to explore the world of Natural Language Processing (NLP) by building what is known as a Sentiment Analysis Model. We will be implementing and comparing both a Naïve Bayes and a Deep Learning LSTM model. |