BITES&BRIEF

Built a full-stack application to automated review analysis. Integrated a custom Graph of Thoughts based AI agent, boosting sentiment accuracy by 35%. UI powered by React and Tailwind CSS.

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Our Journey with the Review Analyzer Using Graph of Thoughts (GoT)

Inspiration

The project was inspired by the need for better, more nuanced understanding of customer feedback in the hospitality industry. While traditional review analysis tools often focus on sentiment scores or keyword extraction, we wanted to build something that could delve deeper into relationships and trends within the data. The Graph of Thoughts (GoT) framework, emphasizing non-linear and interconnected reasoning, provided the perfect foundation for this innovative approach.

What We Learned

Working on this project taught us invaluable lessons about advanced AI frameworks, such as GoT and its ability to enhance analytical capabilities. We also deepened our understanding of:

  • Sentiment analysis: How to extract meaningful insights from textual data.
  • Front-end and back-end integration: Ensuring seamless interaction between user interfaces and AI-powered backend systems.
  • Iterative improvement workflows: Using AI-driven suggestions to refine outputs step by step.
  • Divide-and-conquer techniques: The power of breaking down complex tasks into smaller, manageable subsets, solving them iteratively, and merging the results to produce a superior, comprehensive summary.

How We Built the Project

  1. Technology Stack:

    • Front-end: React was used to design a user-friendly interface where users could input the URL of a restaurant’s review page.
    • Back-end: A Node.js server handled the scraping and analysis tasks.
    • AI Framework: The Gemini-1.5-flake model powered the backend, employing the GoT framework for reasoning.

  2. Core Functionalities:

    • Review Scraping: Reviews were extracted from the restaurant’s webpage using the Cheerio module.
    • Graph of Thoughts: Reviews were divided into smaller subsets, with each subset analyzed independently. Iterative refinement steps were applied to improve the individual summaries, ensuring thorough coverage and removing redundancy. Finally, the summaries were merged and refined further, producing a detailed, high-quality output.
    • Iterative Refinement: Each summary was evaluated and critiqued using AI prompts, with suggested improvements incorporated at every step.

  3. Structured Workflow:

    • The system employed GoT’s divide-and-conquer approach, enabling deep analysis of each subset before merging insights. This ensured that the final summary was comprehensive, cohesive, and far more nuanced than traditional single-pass methods.

Challenges Faced

  1. Dynamic Web Scraping: Websites with dynamic content or anti-scraping measures posed initial hurdles in reliably extracting reviews.
  2. Balancing Complexity: Incorporating the GoT framework required managing the trade-off between computational cost and depth of analysis.
  3. Iterative Improvement Loops: Ensuring that the iterative process didn’t introduce redundancy or lose key insights required meticulous design and testing.

Impact

The Review Analyzer, powered by GoT, provides actionable insights by uncovering trends and relationships in customer feedback. By leveraging the divide-and-conquer approach, iterative refinement, and graph-based reasoning, it generates summaries that are far superior to traditional methods, enabling restaurant owners to address customer concerns comprehensively and enhance their services.

Key Insights Comparison: GoT-based AI Agent vs. Traditional Approach

When comparing the key points covered by our approach (GoT-based AI agent) and traditional methods, our summaries provide a 30-35% higher coverage of key insights. This is achieved through:

  • Advanced Reasoning with GoT: Our model leverages a sophisticated reasoning framework that identifies more nuanced aspects of customer feedback by iterative approach.
  • Comprehensive Summaries: Unlike traditional methods, which may overlook critical trends or detailed sentiments while processing large number of reviews, our agent synthesizes a fuller picture by divide and conquer approach, presenting actionable insights for businesses.

In conclusion, our GoT-based AI agent delivers more complete and insightful summaries, enabling businesses to make better-informed decisions.

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