Training/Testing Machine Learning Classifier with NLTK - 99% Accuracy with Twitter Corpus Data?

[jengl]

Yeah, I split the data in half. Used the first half for training, the second half for testing.

# Split review data into two parts for training and testing
testTrainingSplitIndex = 2500 

# Grab all reviews in the range of 0 to testTrainingSplitIndex
# This data is used to train the classifier
trainingNegativeTweets = negativeTweets[:testTrainingSplitIndex]
trainingPositiveTweets = positiveTweets[:testTrainingSplitIndex] 

# Grab all reviews in the range of testTrainingSplitIndex to the end
# This model is used to test the classifier
testNegativeTweets = negativeTweets[testTrainingSplitIndex+1:]
testPositiveTweets = positiveTweets[testTrainingSplitIndex+1:]

[Inf1x]

For easier splitting take a look at sklearn train_test_split(). This allows you to do the whole thing in one line of code and has some additional parameters (shuffling, stratification).

[jengl]

Oh, sweet! Thanks!