Tweet Sentiment Classification
Implemented IJCA 2015 paper research using Textblob to classify tweets into positive, negative and neutral sentiment in Python
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Designed a model for image classification which improves the learning behavior of Residual Networks and has better accuracy compared to previously published state-of-the-art models with 5.62% error rate, least in existing models The preprint arXiv paper has been cited 10 times and received appreciation from Node.js founder, Ryan Dahl via Twitter Publication: ACM Digital Library ISBN 978-1-4503-4301-5
Implemented IJCA 2015 paper research using Textblob to classify tweets into positive, negative and neutral sentiment in Python
Implemented IJCA 2015 paper research using Textblob to classify tweets into positive, negative and neutral sentiment in Python