Create README.md

README.md

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+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+# Download NLTK resources (only need to run once)
+nltk.download('vader_lexicon')
+
+# Sample text for sentiment analysis
+with open("lks.txt", 'r') as file:
+            fl = file.read()
+        
+contactId = fl.split("|")[0]
+transcript=fl.split("|")[1]
+transcript=transcript.replace("'",'')
+# Initialize the sentiment analyzer
+sia = SentimentIntensityAnalyzer()
+print(transcript)
+
+# Analyze sentiment
+sentiment_score = sia.polarity_scores(transcript)
+
+# Initialize dictionary to store tone counts
+tones = {
+    'analytical': 0,
+    'anger': 0,
+    'confident': 0,
+    'fear': 0,
+    'joy': 0,
+    'sadness': 0,
+    'tentative': 0
+}
+
+# Apply thresholds and count tones
+if sentiment_score['compound'] >= 0.05:  # Threshold for positive sentiment
+    tones['joy'] += 1
+elif sentiment_score['compound'] <= -0.05:  # Threshold for negative sentiment
+    tones['anger'] += 1
+elif sentiment_score['neg'] >= 0.5:  # Threshold for high negativity
+    tones['sadness'] += 1
+elif sentiment_score['pos'] <= 0.2:  # Threshold for low positivity
+    tones['fear'] += 1
+elif sentiment_score['neu'] >= 0.5:  # Threshold for high neutrality
+    tones['tentative'] += 1
+else:  # Otherwise, consider it analytical or confident
+    tones['analytical'] += 1
+    tones['confident'] += 1
+
+# Print tone counts
+print("Tone Counts:", tones)