Create functions.py

functions.py

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+def get_wordnet_pos(treebank_tag):
+    if treebank_tag.startswith('J'):
+        return wordnet.ADJ
+    elif treebank_tag.startswith('V'):
+        return wordnet.VERB
+    elif treebank_tag.startswith('N'):
+        return wordnet.NOUN
+    elif treebank_tag.startswith('R'):
+        return wordnet.ADV
+    else:
+        return wordnet.NOUN
+
+lemmatizer = WordNetLemmatizer()
+
+def preprocess_text(text):
+    text = text.lower() # lowercase text
+    tokens = word_tokenize(text) # tokenize
+    filtered_words = [word for word in tokens if word.lower() not in stopword_list]
+    lemmatized_words = [lemmatizer.lemmatize(w, get_wordnet_pos(w)) for w in filtered_words]
+    lemmatized_clean = [word.translate(str.maketrans('', '', string.punctuation)) for word in lemmatized_words]
+    return ' '.join(lemmatized_clean)
+
+def text_model(df):
+  df['preprocessed_text'] = df['Comment'].apply(preprocess_text)
+  return df['preprocessed_text']
+
+def prediction(model, X):
+  y_pred = model.predict(X)
+  predictions = np.argmax(y_pred, axis=1)
+  for index, val in enumerate(predictions):
+    if val == 0:
+      print(f"Text {index} indicates the person is feeling FEAR")
+    elif val == 1:
+      print(f"Text {index} indicates the person is feeling ANGER")
+    else:
+      print(f"Text {index} indicates the person is feeling JOY")