[function] preprocess_amazon_reviews() - P3chys/textmining GitHub Wiki

Function: preprocess_amazon_reviews()

Purpose

Main orchestrator function that executes the complete text preprocessing pipeline for Amazon reviews data, from raw CSV input to analysis-ready dataset with visualizations.

Syntax

preprocess_amazon_reviews(file_path, sample_size=None, save_processed=True, 
                         output_path='/content/drive/My Drive/Mendelka/TM/processed_amazon_reviews.csv')

Parameters

Parameter Type Default Description
file_path str Required Path to the source CSV file containing Amazon reviews
sample_size int or None None Number of rows to sample from dataset. None loads entire dataset
save_processed bool True Whether to save the final processed DataFrame to file
output_path str Default Google Drive path Destination path for saving processed data

Returns

  • Type: pandas.DataFrame
  • Content: Fully processed dataset ready for text mining analysis

Pipeline Workflow

The function executes the following sequence of operations:

1. Data Loading

df = load_data(file_path, sample_size)
  • Loads raw Amazon reviews data
  • Handles tab-separated values
  • Converts star ratings to numeric format
  • Applies sampling if specified

2. Data Exploration

exploration_results = explore_data(df)
  • Analyzes dataset structure and quality
  • Calculates review length statistics
  • Examines missing values
  • Outputs dataset shape information

3. Text Preprocessing

df_processed = preprocess_dataframe(df)
  • Cleans review text (HTML removal, lowercasing, etc.)
  • Tokenizes text into individual words
  • Adds processed text columns to DataFrame

4. Sentiment Classification

df_processed = create_binary_sentiment(df_processed)
  • Creates binary sentiment labels based on star ratings
  • Typically: 1-3 stars → negative, 4-5 stars → positive
  • Adds sentiment column for analysis

5. N-gram Extraction

df_processed = extract_ngrams(df_processed)
  • Generates 1-grams, 2-grams, and 3-grams
  • Creates both tuple and string representations
  • Enables phrase-level analysis

6. Visualization Generation

plot_rating_distribution(df_processed)
plot_review_length_distribution(df_processed)
plot_sentiment_distribution(df_processed)
  • Creates three visualization files:
    • rating_distribution.png
    • review_length_distribution.png
    • sentiment_distribution.png

7. Data Persistence

df_processed.to_csv(output_path, index=False)
  • Saves processed DataFrame to CSV format
  • Preserves all preprocessing results
  • Enables reuse without reprocessing

Output Structure

The returned DataFrame contains all original columns plus:

Added Preprocessing Columns:

  • review_length - Word count per review
  • cleaned_text - Cleaned review text
  • processed_text - Tokenized review (list format)
  • sentiment - Binary sentiment classification
  • 1grams, 1grams_string - Unigrams
  • 2grams, 2grams_string - Bigrams
  • 3grams, 3grams_string - Trigrams

Logging Output

The function provides progress updates:

Loading data...
Exploring data...
Loaded dataset with shape: (1000000, 15)
Preprocessing text...
Creating binary sentiment labels...
Extracting n-grams...
Generating visualizations...
Saving processed data to /path/to/output.csv...
Preprocessing complete!

Dependencies

Required functions (must be defined before calling):

  • load_data()
  • explore_data()
  • preprocess_dataframe()
  • create_binary_sentiment()
  • extract_ngrams()
  • plot_rating_distribution()
  • plot_review_length_distribution()
  • plot_sentiment_distribution()

Error Handling

No explicit error handling - caller responsible for:

  • Ensuring file_path exists and is readable
  • Verifying output_path directory permissions
  • Managing memory for large datasets

Performance Considerations

  • Memory Usage: Keeps full dataset in memory throughout pipeline
  • Processing Time: Sequential execution means total time = sum of all steps
  • Storage: Output file size approximately 3-4x larger than input due to added columns

Usage Examples

# Process entire dataset
df_full = preprocess_amazon_reviews('amazon_reviews.csv')

# Process sample for testing
df_sample = preprocess_amazon_reviews('amazon_reviews.csv', 
                                    sample_size=10000,
                                    output_path='sample_processed.csv')

# Process without saving
df_temp = preprocess_amazon_reviews('amazon_reviews.csv', 
                                  save_processed=False)

Integration Notes

  • Google Colab: Default output path assumes Google Drive mount
  • Local Environment: Modify output_path for local file systems
  • Batch Processing: For very large datasets, consider chunked processing
  • Resumption: Check if output file exists to avoid reprocessing