Sentiment Analysis on Twitter using Differential Privacy

Environment:

• Python 3.9.5
• RAM: 16GB and 32GB
• GPU: NVIDIA GeForce RTX 2070 and NVIDIA Tesla V100

Dataset: https://www.kaggle.com/datasets/kazanova/sentiment140

• notebooks/baseline contains non-private Sentiment Analysis
• notebooks/dp contains private Sentiment Analysis
• code in notebooks/learning rate is used to obtain the learning rate of the LR-Model
• code in notebooks/preprocessing is used for the preprocessing techniques and for the procedure of saving the resulting datasets to CSV files.

How to run:

• download dataset from https://www.kaggle.com/datasets/kazanova/sentiment140
• change the encoding of the dataset to UTF-8
• run notebooks/preprocessing/remove_tweets.pynb (set TRAINDATA_PATH to the filepath of the downloaded train dataset). This creates the file train_tweets_removed.csv in notebooks/preprocessing/data
• create an empty folder called csv_rows in notebooks/preprocessing/data
• run notebooks/preprocessing/all-preprocessing.ipynb
• run the desired experiments on the preprocessed datasets (they will be saved in notebooks/preprocessing/data/csv_rows, so you might want to change the FILES_DIRECTORY variable leading inside the folder csv_rows)