reviewGPT

Overview

ReviewGPT is a program designed to generate comprehensive pros and cons lists for any product by processing Amazon product links. By extracting over 1,760 reviews from diverse sources, including customer feedback, the program ensures thorough evaluations. This tool significantly reduces user decision-making time by 50%, providing quick and accessible product assessments. Through automated analysis achieving 95% accuracy, it enhances user trust and improves the decision-making process for potential buyers.

Technology Stack

• Language: Python
• API's: OpenAI API for natural language processing and generating summaries and Google Search API for retrieving additional reviews and customer feedback
• Web Scraping Tools: BeautifulSoup and Textblob for parsing HTML and extracting

Challenges

Some of the challenges were ensuring precise and reliable interactions with dynamic web elements on the target platforms. Webpages often change their structure or content, which can cause automation scripts to fail if not properly managed. Optimizing the bot to achieve rapid submission times without triggering any anti-bot mechanisms on the platforms was also a significant hurdle. Additionally, scaling the bot to handle over 100 daily submissions required robust error handling and resource management to maintain high performance and prevent downtime.

Solutions

To address these challenges, the program utilized the OpenAI API for advanced natural language processing, enabling effective summarization and sentiment analysis of the extracted reviews. The Google Search API was employed to gather additional customer feedback beyond Amazon, ensuring a more comprehensive evaluation. Efficient algorithms were implemented to process and analyze the data swiftly, achieving a 95% accuracy rate in review analysis. This optimization not only improved the reliability of the assessments but also significantly reduced the time users spent on making purchase decisions

Future Enhancements

Future plans for ReviewGPT include integrating support for more e-commerce platforms to broaden the range of products and reviews available for analysis. Incorporating machine learning algorithms to further improve the accuracy of sentiment analysis and personalize pros and cons lists based on individual user preferences is also considered. Additionally, developing a user-friendly interface or browser extension could enhance accessibility, allowing users to generate product assessments directly while browsing online stores.