Prakhar Tech Insights

Sentiment Analysis from Call Recording Transcripts Using NLP in Python (Use Case - 2)

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Prakhar Kumar
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2 min read

Understanding customer sentiments is crucial for businesses to make informed decisions and improve customer experiences. Call recordings provide a wealth of valuable information, but analyzing them manually is time-consuming and resource-intensive. In this blog post, we'll explore how to perform sentiment analysis on customer call recording transcripts using Natural Language Processing (NLP) techniques in Python.

The Power of Sentiment Analysis

Sentiment analysis allows businesses to gauge customer sentiments, emotions, and opinions expressed in call recordings. By analyzing these sentiments, companies can identify areas for improvement, detect customer satisfaction levels, and make data-driven decisions.

Step 1: Text Preprocessing

The first step is to preprocess the call recording transcripts to prepare them for sentiment analysis. This includes tokenization, removing stopwords, and text normalization.

pythonCopy codeimport nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Sample call recording transcript
call_transcript = "..."
tokens = word_tokenize(call_transcript)
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

processed_words = [lemmatizer.lemmatize(word.lower()) for word in tokens if word.isalnum() and word.lower() not in stop_words]

Step 2: Sentiment Analysis

Next, we'll use sentiment analysis techniques to determine the sentiment expressed in the call recording. We'll use VaderSentiment from the NLTK library for this purpose.

pythonCopy codefrom nltk.sentiment import SentimentIntensityAnalyzer

analyzer = SentimentIntensityAnalyzer()
sentiment_score = analyzer.polarity_scores(call_transcript)
sentiment_label = 'Positive' if sentiment_score['compound'] > 0 else 'Negative' if sentiment_score['compound'] < 0 else 'Neutral'

Step 3: Visualization and Insights

After analyzing the sentiment, visualize the results to gain insights into customer sentiments. You can create visualizations such as sentiment distribution charts or word clouds to understand the prevalent sentiments and key topics discussed.

pythonCopy codeimport matplotlib.pyplot as plt

# Example visualization: Sentiment distribution chart
sentiments = ['Positive', 'Neutral', 'Negative']
counts = [sentiment_score[s] for s in ['pos', 'neu', 'neg']]
plt.bar(sentiments, counts)
plt.xlabel('Sentiment')
plt.ylabel('Count')
plt.title('Sentiment Distribution in Call Recording')
plt.show()

Benefits of Sentiment Analysis on Call Recordings

  • Customer Insights: Gain deep insights into customer sentiments, preferences, and pain points.

  • Proactive Decision-Making: Identify issues early and take proactive measures to address customer concerns.

  • Enhanced Customer Experience: Tailor services and solutions based on customer feedback and sentiments.

Conclusion

Sentiment analysis on customer call recording transcripts empowers businesses to understand customer sentiments effectively. By leveraging NLP techniques in Python, you can automate sentiment analysis processes, derive actionable insights, and enhance customer experiences.