Use float instead of str as Category

unimore_bda_6/__main__.py

@@ -19,13 +19,13 @@ def main():
 
     for dataset_func in [polar_dataset, varied_dataset]:
         for SentimentAnalyzer in [
-            # NLTKSentimentAnalyzer,
-            TensorflowSentimentAnalyzer,
+            NLTKSentimentAnalyzer,
+            # TensorflowSentimentAnalyzer,
         ]:
             for Tokenizer in [
-                # NLTKWordTokenizer,
-                # PottsTokenizer,
-                # PottsTokenizerWithNegation,
+                NLTKWordTokenizer,
+                PottsTokenizer,
+                PottsTokenizerWithNegation,
                 LowercaseTokenizer,
             ]:
                 tokenizer = Tokenizer()

unimore_bda_6/database.py

@@ -26,7 +26,7 @@ class Review(t.TypedDict):
 
 
 Text = str
-Category = str
+Category = float
 DataTuple = collections.namedtuple("DataTuple", ["text", "category"])
 DataSet = t.Iterable[DataTuple]
 
@@ -101,23 +101,9 @@ def review_to_datatuple(review: Review) -> DataTuple:
     * unknown (everything else)
     """
     text = review["reviewText"]
-    rating = review["overall"]
+    category = review["overall"]
 
-    match rating:
-        case 1.0:
-            category = "terrible"
-        case 2.0:
-            category = "negative"
-        case 3.0:
-            category = "mixed"
-        case 4.0:
-            category = "positive"
-        case 5.0:
-            category = "great"
-        case _:
-            category = "unknown"
-
-    return DataTuple(text, category)
+    return DataTuple(text=text, category=category)
 
 
 def polar_dataset(collection: pymongo.collection.Collection, amount: int) -> t.Iterator[DataTuple]: