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- documentBuilder.py - This is the first step in training the sentiment classifiers. It uses preclassified tweets and does some of the initial preprocessing of them.
- featuresetBuilder.py - Continues the preprocessing of the training set and outputs a feature set to use for training.
- SentimentTrainerFromPickles.py - Uses the above feature set to train all of our different sentiment classifiers. It then serializes and saves them to be used.
- SentimentAnalyzer.py - Loads the stored sentiment classifiers and outputs the classification and confidence for a given string.
- twitterCheckerToSQL.py - Streams tweets from Twitter, processes them, uses the SentimentAnalyzer.py to classify them, and stores the results in an SQL DB.
- ScrapeBoxOffice.py - Pulls the release date and daily box office numbers for each movie from the web, and stores them in our SQL DB.
- plotFromSQL.py - Pulls all of the twitter results and box office data and plots it and stores those as .png files.
- decisionTreeClassifierBuilder.py - Pulls all of the data from the DB for selected movies, converts those into the feature set we are using, and uses that to build a decision tree. The decision tree is saved for future use.
- boxOfficeDecisionTreeClassifier.py - Loads the decision tree, pulls data for a given movie from the DB, process its data into the relevant features, and pass those features through the tree to get a week by week prediction of the next weeks box office performance change.
The pickled_objects folder holds copies of the classifiers output by the SentimentTrainerFromPickles.py script. Those are loaded by the SentimentAnalyzer.py script and used to classify the text. Those can be used instead of building and retraining everything from scratch, if desired.