diff --git a/chatterbot/logic/time_adapter.py b/chatterbot/logic/time_adapter.py
index 88eb0d1e2..afe479cbf 100644
--- a/chatterbot/logic/time_adapter.py
+++ b/chatterbot/logic/time_adapter.py
@@ -10,27 +10,56 @@ class TimeLogicAdapter(LogicAdapter):
 
     def __init__(self, **kwargs):
         super(TimeLogicAdapter, self).__init__(**kwargs)
-        from textblob.classifiers import NaiveBayesClassifier
-
-        training_data = [
-            ('what time is it', 1),
-            ('do you know the time', 1),
-            ('do you know what time it is', 1),
-            ('what is the time', 1),
-            ('it is time to go to sleep', 0),
-            ('what is your favorite color', 0),
-            ('i had a great time', 0),
-            ('what is', 0)
+        from nltk import NaiveBayesClassifier
+
+        self.positive = [
+            'what time is it',
+            'do you know the time',
+            'do you know what time it is',
+            'what is the time'
+        ]
+
+        self.negative = [
+            'it is time to go to sleep',
+            'what is your favorite color',
+            'i had a great time',
+            'what is'
         ]
 
-        self.classifier = NaiveBayesClassifier(training_data)
+        labeled_data = (
+            [(name, 0) for name in self.negative] +
+            [(name, 1) for name in self.positive]
+        )
+
+        # train_set = apply_features(self.time_question_features, training_data)
+        train_set = [(self.time_question_features(n), text) for (n, text) in labeled_data]
+
+        self.classifier = NaiveBayesClassifier.train(train_set)
+
+    def time_question_features(self, text):
+        """
+        Provide an analysis of significan features in the string.
+        """
+        features = {}
+
+        all_words = " ".join(self.positive + self.negative).split()
+
+        for word in text.split():
+            features['contains({})'.format(word)] = (word in all_words)
+
+        for letter in 'abcdefghijklmnopqrstuvwxyz':
+            features['count({})'.format(letter)] = text.lower().count(letter)
+            features['has({})'.format(letter)] = (letter in text.lower())
+
+        return features
 
     def process(self, statement):
         from chatterbot.conversation import Statement
 
         now = datetime.now()
 
-        confidence = self.classifier.classify(statement.text.lower())
+        time_features = self.time_question_features(statement.text.lower())
+        confidence = self.classifier.classify(time_features)
         response = Statement('The current time is ' + now.strftime('%I:%M %p'))
 
         return confidence, response