Updated sentiment analysis component and overall project configuration

config.yml

@@ -6,7 +6,10 @@ pipeline:
 # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
 # # If you'd like to customize it, uncomment and adjust the pipeline.
 # # See https://rasa.com/docs/rasa/tuning-your-model for more information.
-   - name: WhitespaceTokenizer
+   - name: SpacyNLP
+   - name: SpacyTokenizer
+   - name: sentiment.SentimentAnalyzer
+   - name: SpacyFeaturizer
    - name: RegexFeaturizer
    - name: LexicalSyntacticFeaturizer
    - name: CountVectorsFeaturizer

sentiment.py

@@ -38,14 +38,19 @@ def process(self, message, **kwargs):
             and append the prediction results to the message class."""
 
         sid = SentimentIntensityAnalyzer()
-        res = sid.polarity_scores(message.text)
+        data = ""
+        try:
+            data = message.data['text']
+        except KeyError:
+            pass
+        res = sid.polarity_scores(data)
         key, value = max(res.items(), key=lambda x: x[1])
 
         entity = self.convert_to_rasa(key, value)
 
         message.set("entities", [entity], add_to_output=True)
 
-    def persist(self, model_dir):
+    def persist(self, file_name, model_dir):
         """Pass because a pre-trained model is already persisted"""
 
         pass