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@@ -13,24 +13,24 @@ Data for this in-class competition comes from the [Sentiment140](https://www.kag
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## Getting started using Lexicon and Machine Learning (ML) based methods
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Open `SentimentAnalysis.ipynb` on a jupyter notebook environment, or [](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis.ipynb)
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- VADER (VALENCE based sentiment analyzer) (67%)
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- VADER (VALENCE based sentiment analyzer) [67%]
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- Naive Bayes
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- Linear SVM (Support Vector Machine) (80%)
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- Linear SVM (Support Vector Machine) [80%]
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- Decision Tree
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- Random Forest
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- Extra Trees
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- SVC (80%)
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- SVC [80%]
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## Exploring using Deep Learning Techniques (LSTM)
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Open `SentimentAnalysis_RNN.ipynb` on a jupyter notebook environment, or [](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis_RNN.ipynb)
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The LSTM deep learning method (79%) did not perform better than SVC/SVM method
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The LSTM deep learning method [79%] did not perform better than SVC/SVM method
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<br/>
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## How about the BERT Transformers model?
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Open `SentimentAnalysis_BERT.ipynb` on a jupyter notebook environment, or [](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis_BERT.ipynb)
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The State-of-the-Art transformer model performs slightly better at 82% accuracy
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The State-of-the-Art transformer model performs slightly better at [82%] accuracy
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