Skill 032

Text Sentiment Analysis

Analyze customer reviews and extract sentiment scores to understand customer opinions and trends over time.

Problem

Given a dataset of customer reviews, the goal is to evaluate the sentiment (positive, negative, neutral) expressed in each review. This provides valuable insights into customer satisfaction and product performance.

Methodology

1. Text Preprocessing: Clean and prepare the text data by removing stop words, punctuation, and applying lemmatization.
2. Sentiment Scoring: Use pre-trained sentiment analysis models or libraries to score the sentiment of each review.
3. Aggregation: Summarize sentiment scores by product category to identify trends and areas for improvement.

Output

A DataFrame with added columns:

• review: Original customer review
• sentiment_score: Float sentiment score (e.g., from -1 to 1)
• sentiment_label: Categorical sentiment label (positive, negative, neutral)

Installation

pip install pandas numpy nltk transformers
python -c "import nltk; nltk.download('stopwords')"

3-Step Pipeline

Step 1: Load and Preprocess Reviews (step1_load_and_preprocess.py)

• Input: CSV file of customer reviews with a review column.
• Process: Normalize text, tokenize words, remove stop words, and lemmatize.
• Output: Cleaned DataFrame with review column ready for analysis.

Step 2: Analyze Sentiment (step2_sentiment_analysis.py)

• Input: Cleaned DataFrame from Step 1.
• Process: Apply sentiment analysis model to generate scores and labels for each review.
• Output: DataFrame with added sentiment_score and sentiment_label columns.

Step 3: Summarize Results (step3_aggregate_results.py)

• Input: DataFrame from Step 2.
• Process: Group by product category and compute average sentiment scores.
• Output: Summary DataFrame of average sentiment scores by category.

# Example of sentiment analysis using Hugging Face Transformers
from transformers import pipeline

# Load sentiment analysis pipeline
sentiment_pipeline = pipeline('sentiment-analysis')

# Example review
review = "This product is amazing!"
result = sentiment_pipeline(review)
print(result)

This skill provides a comprehensive approach to extracting sentiment from customer reviews, which can help businesses improve their products and services.