From 611453739e410f671522daa9dfe1f6538ee1fc0d Mon Sep 17 00:00:00 2001
From: Nilotpal Basu <133061451+nilotpal-basu@users.noreply.github.com>
Date: Sun, 21 Jul 2024 23:22:19 +0530
Subject: [PATCH] Update README.md

---
 README.md | 115 ++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 95 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index 2966637..be57cd7 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,110 @@
+![Logo](https://github.com/Terminal127/sen-analysis-intel/blob/main/Default_sentimental_analysis_using_machine_learning_0.jpg)
+
+
 # Intel Products Sentiment Analysis
 
-This project performs sentiment analysis on Intel product reviews using RoBERTa. The goal is to clean the data, analyze the sentiment of the reviews, and provide actionable insights and recommendations for improving Intel products based on customer feedback.
+This project analyzes customer sentiment towards Intel products using a multi-stage approach.
+
+## Project Overview
+
+1. **Data Gathering**: Web scraping customer reviews from Amazon using Selenium.
+
+2. **Sentiment Analysis & Visualization**:
+   - Data cleaning
+   - Dual sentiment analysis: NLTK's VADER and Hugging Face Transformers (BERTweet)
+   - Visualization of sentiment distribution
+
+3. **Aspect-Based Sentiment Analysis (ABSA)**:
+   - Identify problematic aspects in negative reviews using spaCy
+   - Analyze aspect-specific sentiment with a fine-tuned DeBERTa model
 
-## Files
+4. **Insights and Actionable Recommendations**:
+   - Provide deep understanding of customer feedback
+   - Guide product improvements, marketing strategies, and customer support
 
-- `Intel_Products_Sentiment_Analysis_RoBERTa.ipynb`: The Jupyter Notebook with the analysis.
-- `reviews.csv`: The dataset with reviews.
+## Documentation
 
-## Instructions
+### Python Script:
+
+The script defines a main function that takes various parameters such as the URL of the Amazon product review page, the number of pages to scrape, the output CSV file name, wait time for elements to load, the number of retries, and an optional proxy server address.
+
+- The script sets up a headless browser using Selenium to navigate to the given URL and scrape reviews from the specified number of pages.
+- The scraped reviews are saved to a CSV file.
+
+### links.txt File:
+
+This file contains a list of URLs, each pointing to an Amazon product review page.
 
-1. **Data Cleaning**: Remove missing values, clean the text by converting to lowercase, removing newlines, and removing punctuation.
-2. **Sentiment Analysis**: Use the RoBERTa model to analyze the sentiment of the cleaned reviews.
-3. **Insights and Recommendations**: Based on the sentiment analysis, provide insights and recommendations for product improvement.
 
 ## Usage
 
 1. Clone the repository:
-    ```sh
-    git clone https://github.com/TensorTrove/intel-products-sentiment-analysis.git
-    ```
+   ```sh
+   git clone https://github.com/TensorTrove/intel-products-sentiment-analysis.git
+   ```
 
 2. Navigate to the project directory:
-    ```sh
-    cd intel-products-sentiment-analysis
-    ```
+   ```sh
+   cd intel-products-sentiment-analysis
+   ```
+
+3. Run the python file:
+   ```sh
+   python3 comments.py
+   # it will generate reviews.csv
+   ```
+
+4. Clean the data:
+   ```sh
+   python3 clean_reviews.py
+   # it will generate cleaned_reviews_textblob.csv
+   ```
+
+5. Run the Jupyter Notebooks for different models:
+   - Intel_Products_Sentiment_Analysis_CNN.ipynb
+   - Intel_Products_Sentiment_Analysis_FastText.ipynb
+   - Intel_Products_Sentiment_Analysis_LSTM.ipynb
+   - Intel_Products_Sentiment_Analysis_NLTK.ipynb
+   - Intel_Products_Sentiment_Analysis_NLTK_With_translation.ipynb
+   - Intel_Products_Sentiment_Analysis_RoBERTa.ipynb
+   - Intel_Products_Sentiment_Analysis_ULMFiT.ipynb
+   - Intel_Products_Sentiment_Analysis_VADER.ipynb
+
+6. Run the ASBA for Sentiment Analysis: Analyze the sentiment of reviews using the DeBERTa model, focusing on specific aspects of Intel products:
+- Performance
+- Battery
+- Design
+- Value
+
+**Example:**
+   ```sh
+   jupyter notebook Intel_Products_Aspect_Based_Sentiment_Analysis_DeBERTa.ipynb
+   ```
+   
+
+## Contributing
+
+Contributions are always welcome!
+
+## Recent Updates
+
+- Added CNN model for sentiment analysis
+- Implemented FastText and LSTM models
+- Added NLTK analysis with and without translation
+- Incorporated RoBERTa model for sentiment analysis
+- Implemented ULMFiT model
+- Added VADER sentiment analysis
+
+## Authors
+
+- [@ankitdey-marsh](https://www.github.com/ankitdey-marsh)
+- [@Terminal127](https://www.github.com/Terminal127)
+- [@debjit-mandal](https://www.github.com/debjit-mandal)
+- [@nilotpal-basu](https://www.github.com/nilotpal-basu)
+- [@MrCelestial](https://www.github.com/MrCelestial)
+
+------------------------------------------------------------------------------------------------------------
 
-3. Open the Jupyter Notebook:
-    ```sh
-    jupyter notebook Intel_Products_Sentiment_Analysis_RoBERTa.ipynb
-    ```
+Feel free to explore and run these notebooks to compare different sentiment analysis approaches.
 
-## License
 
-This project is licensed under the GNU General Public License v3.0.