This project focuses on analyzing customer sentiment through the implementation of the NLTK (Natural Language Toolkit) algorithm on product web reviews. The process involves extracting data from a SQL database, applying sentiment analysis using Python, generating CSV files, and then visualizing the data in Power BI for deeper insights.

• Python 3.x
• NLTK (Natural Language Toolkit)
• SQL Server (or any SQL database)
• Power BI Desktop (for visualization)

1. Database Setup:

   • Ensure the availability of the SQL database containing the product web reviews.
   • Make sure the necessary credentials and permissions are set up for access.

2. Python Script for SQL Extraction (extract_data.py):

   • Utilize this script to connect to the SQL database and extract relevant data.
   • Ensure that the script handles any necessary data transformations or filtering required.

3. Python Script for NLTK Algorithm (sentiment_analysis.py):

   • This script implements the NLTK algorithm for sentiment analysis.
   • It takes the extracted data as input and generates sentiment scores for each review.

4. Python Script for Generating CSV File (generate_csv.py):

   • After sentiment analysis, use this script to generate CSV files containing the processed data.
   • Customize the script to include any additional metadata or formatting required.

5. Automatic Upload of CSV File in Power BI:

   • Set up an automated process or schedule to upload the generated CSV files to Power BI.
   • Utilize Power BI APIs or any relevant integration tools for seamless data transfer.

6. Data Processing in Power BI:

   • Upon uploading the CSV files, perform necessary data processing steps in Power BI.
   • This may include data cleansing, transformation, and merging with other datasets if needed.

7. Visualization in Power BI:

   • Leverage Power BI's visualization capabilities to create insightful dashboards and reports.
   • Design visualizations that effectively communicate customer sentiment trends and patterns.