This guide demonstrates how to use pandas.apply for sentiment analysis on customer reviews. We'll classify reviews as Positive, Negative, or Neutral using a custom function.

Problem Statement

You have a dataset of customer reviews and want to classify each review based on its sentiment using a polarity score.

Dataset Example

import pandas as pd

# Sample DataFrame
data = {'Review': [
    "The product is fantastic, I love it!",
    "Terrible experience, I will not buy again.",
    "It's okay, but could be better."
]}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

Output:

																	Review
0        The product is fantastic, I love it!
1     Terrible experience, I will not buy again.
2                  It's okay, but could be better.

Solution: Sentiment Classification with pandas.apply

Step 1: Install the Required Library

For this use case, we'll use the TextBlob library for sentiment analysis. Install it using pip if not already installed:

pip install textblob

Step 2: Define the Sentiment Analysis Function

from textblob import TextBlob

# Function to classify sentiment
def classify_sentiment(review):
    analysis = TextBlob(review)
    if analysis.sentiment.polarity > 0.2:
        return "Positive"
    elif analysis.sentiment.polarity < -0.2:
        return "Negative"
    else:
        return "Neutral"

Step 3: Apply the Function to the DataFrame

Use the pandas.apply method to classify each review based on its sentiment.

# Apply sentiment classification to the 'Review' column
df['Sentiment'] = df['Review'].apply(classify_sentiment)

# Display the updated DataFrame
print(df)

Sentiment Analysis

Explanation of the Workflow

1. Custom Function: The classify_sentiment function uses TextBlob to calculate the sentiment polarity:

• Polarity > 0.2: Positive sentiment
• Polarity < -0.2: Negative sentiment
• Otherwise: Neutral sentiment

2. Apply Function: The apply method applies the classify_sentiment function to each review in the Review column, efficiently processing each row.

Why Use pandas.apply?

• Flexibility: Allows custom logic for specific data transformations.
• Scalability: Handles large datasets effectively for row- or column-wise operations.
• Ease of Use: Simplifies applying NLP models or custom functions.

Conclusion

Using pandas.apply is a simple and flexible way to perform sentiment analysis and other custom row- or column-based transformations. While it is versatile, keep in mind that vectorized alternatives may offer better performance in some cases. For sentiment analysis, TextBlob is an excellent choice for basic tasks, but for more advanced use cases, libraries like NLTK or spaCy might be more suitable.

For further learning, refer to the pandas documentation and the TextBlob documentation.