• This project performs sentiment analysis on customer reviews using Natural Language Processing (NLP).
• It uses NLTK toolkit for NLP and Beautifulsoup for web scraping of customer reviews.
• The aim of the project is to identify whether the reviews are positive, negative or neutral. TextBlob is used to find the subjectivity and polarity of the reviews, and then sentiment is determined based on the scores.

The data used in this project is collected by web scraping of customer reviews of a product using Beautifulsoup. All reviews of the product were scraped and stored in a CSV file.

Python 3.8 or higher NLTK TextBlob Beautifulsoup4 pandas Running the code To run this code, follow these steps:

Install the dependencies using pip install -r requirements.txt Open the Jupyter notebook file, sentiment_analysis.ipynb Run the code cell by cell to perform sentiment analysis on customer reviews Conclusion After performing sentiment analysis on the customer reviews, the conclusion is stated in the Jupyter notebook file. The sentiment of the reviews is classified into positive, negative or neutral based on the polarity and subjectivity scores. This project can be used to analyze customer sentiment for various products and services.