refactor: refactor sentiment analyzer by removing dead and slow perfo…

…mance code (#2804)

* refactor: refactor sentiment analyzer by removing dead and slow perfomance code

* refactor: refactor sentiment analyzer by removing dead and slow perfomance code

* fix: add not to false boolean values

* Refactor: Add sentiment keyword in all_words

Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com>

nltk/sentiment/sentiment_analyzer.py

@@ -47,10 +47,10 @@ def all_words(self, documents, labeled=None):
         all_words = []
         if labeled is None:
             labeled = documents and isinstance(documents[0], tuple)
-        if labeled == True:
-            for words, sentiment in documents:
+        if labeled:
+            for words, _sentiment in documents:
                 all_words.extend(words)
-        elif labeled == False:
+        elif not labeled:
             for words in documents:
                 all_words.extend(words)
         return all_words
@@ -218,7 +218,7 @@ def evaluate(
             classifier = self.classifier
         print(f"Evaluating {type(classifier).__name__} results...")
         metrics_results = {}
-        if accuracy == True:
+        if accuracy:
             accuracy_score = eval_accuracy(classifier, test_set)
             metrics_results["Accuracy"] = accuracy_score
 
@@ -232,22 +232,22 @@ def evaluate(
             test_results[observed].add(i)
 
         for label in labels:
-            if precision == True:
+            if precision:
                 precision_score = eval_precision(
                     gold_results[label], test_results[label]
                 )
                 metrics_results[f"Precision [{label}]"] = precision_score
-            if recall == True:
+            if recall:
                 recall_score = eval_recall(gold_results[label], test_results[label])
                 metrics_results[f"Recall [{label}]"] = recall_score
-            if f_measure == True:
+            if f_measure:
                 f_measure_score = eval_f_measure(
                     gold_results[label], test_results[label]
                 )
                 metrics_results[f"F-measure [{label}]"] = f_measure_score
 
         # Print evaluation results (in alphabetical order)
-        if verbose == True:
+        if verbose:
             for result in sorted(metrics_results):
                 print(f"{result}: {metrics_results[result]}")