brexit-tweet analysis

Summary

This project is about text engineering and machine learning operations. The topic of this work is, to present the relation between Twitter and Brexit. For training, tweets are taken from some politicians' tweets which include Brexit words between 2015-04-30 and 2017-04-30. After the model created, to test the model, randomly 10.000 tweets taken from Twitter API between 2015-04-30 - 2016-04-30. You can take a look brief presentation and results in Twitter - Brexit Analysis.pdf file.

Steps

1. First, tweets taken from Twitter API and collected in a CSV file. -> You can take a look at getTweets.py
2. Then these tweets classified by hand as positive negative and notr. original-tweets.csv is classified tweets that is going to train. In classes; 1 represents for want to exit, 0 represents for want to stay and 2 represents for inactive.
3. Data cleaning operations takes places in data_cleaner.py . data_cleaner.py file is also main class for the project.
4. Last steps for training operations in model.py file.
5. And then, finally, model can be tested with test.py file.