Data used to train the model was Twitter_by_Country data provided by professor Jonathan Dunn. It was too large to include in this repo. Tripadvisor review data is from Kaggle and is included in the repo in addition to the newly country-labeled datasets.
The process of creating and using the model is split up into two files Project1_createModel.ipynb and Project1_useModel.ipynb. Each Jupyter notebook walks through the process of training a model and then using the pretrained model on new data. The pretrained models have been python pickled and are in unigram_tweet_classification_model.p, bigram_tweet_classification_model.p, and trigram_tweet_classification_model.p. To use them, see the code in the Project1_useModel.ipynb notebook.
Results from testing data on the N-gram models are provided in unigram_tweet_classification_results.txt, bigram_tweet_classification_results.txt, and trigram_tweet_classification_results.txt.
Tripadvisor data with country labeles from each model are included in the repo.
All graphs were created using the code in Project1_createGraphs.ipynb.