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@@ -11,7 +11,7 @@ Recommended to install [Anaconda](https://www.anaconda.com/products/distribution
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Data for this in-class competition comes from the [Sentiment140](https://www.kaggle.com/datasets/kazanova/sentiment140) dataset where the training and test data consists of randomly sampled 10% and 5% of the dataset.
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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. Alternatively, you can view the codes in Google Colab [](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis.ipynb).
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Open `SentimentAnalysis.ipynb` on a jupyter notebook environment, or in Google Colab [](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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- Naive Bayes
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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. Alternatively, you can view the codes in Google Colab [](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis_RNN.ipynb).
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Open `SentimentAnalysis_RNN.ipynb` on a jupyter notebook environment, or in Google Colab [](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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## How about the BERT Transformers model
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Open `SentimentAnalysis_BERT.ipynb` on a jupyter notebook environment. Alternatively, you can view the codes in Google Colab [](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis_BERT.ipynb).
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Open `SentimentAnalysis_BERT.ipynb` on a jupyter notebook environment, or in Google Colab [](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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