added Vader sentiment analysis methods for stronger analysis

requirements.txt

@@ -6,3 +6,5 @@ requests
 textblob
 streamlit
 black
+nltk
+

src/sentiment.py

@@ -1,11 +1,44 @@
+import nltk
 from textblob import TextBlob
+from nltk.sentiment import SentimentIntensityAnalyzer
+nltk.download('vader_lexicon')
 
-
-def analyze_sentiment(text):
+def analyze_sentiment_tb(text):
     polarity = TextBlob(text).sentiment.polarity
     if polarity > 0:
         return "positive"
     elif polarity < 0:
         return "negative"
     else:
         return "neutral"
+
+
+def analyze_sentiment_vader(text):
+    vader_sentiment = SentimentIntensityAnalyzer()
+    polarity = vader_sentiment.polarity_scores(text)
+
+    if polarity["compound"] >= 0.05:
+        return "positive"
+    elif polarity["compound"] <= -0.05:
+        return "negative"
+    else:
+        return "neutral"
+
+
+def analyze_sentiment(text):
+    tb_result = analyze_sentiment_tb(text)
+
+    vader = SentimentIntensityAnalyzer()
+    vader_scores = vader.polarity_scores(text)
+    vader_result = analyze_sentiment_vader(text)
+
+    # If both methods agree, return that sentiment
+    if tb_result == vader_result:
+        return tb_result
+
+    # If they disagree, trust VADER when sentiment is strong
+    if abs(vader_scores["compound"]) >= 0.3:
+        return vader_result
+
+    # Otherwise, neutral
+    return "neutral"