Tweet Classifier

A simple class project to classify a sample data-set of 1200 tweets to either positive or negative class, based on the type of words used in those tweet.

Path to training files:

All data are located in the data directory.
PLUS_TRAINING_DATA = data/processed_plus_data.txt
MINUS_TRAINING_DATA = data/processed_minus_data.txt

Instructions:

First do a pip install -r requirements.txt to install the required modules.

• To train the models, run run_training.py. First you should enter path to training data with PLUS_TRAINING_DATA and then with MINUS_TRAINING_DATA. After that you should select an algorithm to train. Options are naiveBayes, logisticRegression, treeClassifier.
• To test the trained models, run run_test.py. For the first input enter PLUS_TRAINING_DATA and MINUS_TRAINING_DATA. Then choose a model from KNN, naiveBayes, logisticRegression, treeClassifier, finalModel. Then enter the path to the test data file like data/test.txt. At last, enter path to label file for test data. Sample files are provided in data directory.
• To evaluate models, run run_estimation.py. For evaluating, just enter PLUS_TRAINING_DATA and MINUS_TRAINING_DATA as parameters.