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)
sample output
#Tone Counts: {'analytical': 0, 'anger': 0, 'confident': 0, 'fear': 0, 'joy': 1, 'sadness': 0, 'tentative': 0}
