Twitterqa
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Deep Learning Nlp Rl Papers | 286 | 5 years ago | Python | |||||||
| Recent Deep Learning papers in NLU and RL | ||||||||||
| Kelpnet | 230 | 5 months ago | 1 | apache-2.0 | C# | |||||
| Pure C# machine learning framework | ||||||||||
| Datastories Semeval2017 Task4 | 171 | 6 years ago | 8 | mit | Python | |||||
| Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis". | ||||||||||
| Twitterqa | 150 | 7 years ago | n,ull | apache-2.0 | Python | |||||
| Deep learning based Twitter Imposter Chatbot | ||||||||||
| Twitterner | 134 | 2 years ago | 1 | gpl-3.0 | Jupyter Notebook | |||||
| Twitter named entity extraction for WNUT 2016 http://noisy-text.github.io/2016/ner-shared-task.html | ||||||||||
| 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. | ||||||||||
| Twitter Emotion Recognition | 116 | 3 years ago | 5 | agpl-3.0 | Python | |||||
| Models for predicting emotions from English tweets. | ||||||||||
| Branch Example Deep Linking Branchster Android | 96 | 6 months ago | 6 | mit | Java | |||||
| Branch Metrics Example application for Android mobile deep linking / deeplinking - the Branchster app. Branch helps mobile apps grow with deep links / deeplinks that power referral systems, sharing links and invites with full attribution and analytics. | ||||||||||
| Protest Detection Violence Estimation | 87 | 6 years ago | 5 | mit | Jupyter Notebook | |||||
| Implementation of the model used in the paper Protest Activity Detection and Perceived Violence Estimation from Social Media Images (ACM Multimedia 2017) | ||||||||||
| Wine Deep Learning | 78 | 4 years ago | Jupyter Notebook | |||||||
| Exploring applications of deep learning to the world of wine | ||||||||||
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