Amazon Alexa Reviews Sentiment Analysis

This Python script conducts sentiment analysis on Amazon Alexa reviews using the VADER sentiment analysis tool. The analysis includes creating a pie chart to visualize the distribution of ratings and determining the overall sentiment (Positive, Negative, or Neutral) based on the reviews.

Code Structure

1. Importing Libraries:

   • Libraries such as pandas, seaborn, matplotlib.pyplot, and nltk.sentiment.vader are imported for sentiment analysis.

2. Reading Data:

   • Amazon reviews data is loaded into a Pandas DataFrame using pd.read_csv, specifying a custom delimiter as '\t' (tab-separated).

3. Pie Chart of Ratings:

   • The script generates a pie chart illustrating the distribution of ratings using Seaborn and Matplotlib.

4. Sentiment Analysis:

   • Utilizing the VADER sentiment analysis tool from NLTK, the script calculates positive, negative, and neutral sentiments for each review.

5. Overall Sentiment:

   • Total positive, negative, and neutral scores are summed up, and a function (sentiment_score) determines the overall sentiment based on the highest score.

How to Use

• Ensure required libraries are installed (pandas, seaborn, matplotlib, nltk).
• Replace "Amazon.txt" with the path to your dataset.
• Run the script.
• Feel free to customize colors, titles, or adapt the code to your specific use case.

Dependencies

• pandas
• seaborn
• matplotlib
• nltk

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Honey Patel