AI-Powered Product Review Analyzer [by TrustLevel]

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Dominik Tilman
Project Owner

AI-Powered Product Review Analyzer [by TrustLevel]

Funding Requested

$78,000 USD

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Overview

Product reviews are increasingly unreliable due to the growing spread of manipulated reviews. We are therefore developing an AI tool (available via SNET's AI Marketplace) that generates an adjusted, more reliable product review and product rating by collecting reviews from different e-commerce platforms for a searched product and using AI to detect and remove bias and manipulation.

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

The AI-Enhanced Product Review Analyzer will contribute to the growth of SingularityNET by:

  1. Expanding Service Offerings: Adding a valuable tool that leverages advanced AI for unbiased product reviews.
  2. Attracting New Users and Partners: Attracting a diverse user base and drawing in e-commerce platforms and market research firms for potential collaborations.
  3. Generating Revenue for the SNET community with our shared revenue model.

Our Team

We have already created a POC for bias detection (see TrustLevel Github) and a framework to train the model on different use cases. In addition, we have already integrated a service into SNET (at the time of the proposal, the service is deployed on the mainnet, only the publishing on the marketplace is still missing) and thus have valuable experience for this very complex part. 

View Team

AI services (New or Existing)

TrustLevel Product Review Analyzer

Type

New AI service

Purpose

This AI-Powered Product Review Analyzer aims to revolutionize how consumers and businesses interact with product reviews. With the explosion of e-commerce the reliance on user reviews for purchasing decisions has increased significantly. However the prevalence of biased fake or manipulated reviews undermines the reliability of this crucial feedback mechanism.

AI inputs

Product Name or URL

AI outputs

1. Balanced Review Summary: Summary of key pros and cons from collected reviews. 2. Adjusted Overall Rating: Balanced rating after bias correction.

Company Name (if applicable)

TrustLevel

The core problem we are aiming to solve

In the digital age, e-commerce has become a cornerstone of global trade and millions of products are sold online. However, the reliability of product reviews - an important aspect influencing consumers' purchasing decisions - is often compromised by biased or fake reviews. These manipulated reviews can mislead consumers, undermine trust and have a negative impact on legitimate businesses. Our project seeks to solve this problem by providing a tool that collects and analyses product reviews using advanced AI techniques to ensure unbiased and comprehensive reviews.

The core problem we want to solve with our project is the widespread prevalence of biased, fake and unreliable product reviews on e-commerce platforms. It is very difficult for consumers to recognise the authenticity of reviews, which leads to poor purchasing decisions and mistrust. Companies also suffer from their genuine feedback being overshadowed by manipulated reviews. The lack of reliable tools to analyse reviews further exacerbates this problem.

Our specific solution to this problem

The objective for this proposal is to develop an MVP version of the AI-Powered Product Review Analyser, which will be integrated into SingularityNET's AI Marketplace to provide unbiased, comprehensive product reviews for consumers and businesses.

Key Features of MVP:

  1. Review Aggregation
    • Collect reviews from at least three major e-commerce platforms
    • Implement a scalable architecture for adding more platforms in the future.
  2. Fake and Bias Detection
    • Use basic sentiment analysis to identify overly positive or negative reviews.
    • Implement initial algorithms to detect common patterns of fake reviews.
    • Apply customed LLM to identify biases in product reviews. 
  3. Overall Rating Calculation
    • Calculate a balanced overall rating by removing biased and fake reviews.
    • Display adjusted ratings and original ratings for comparison.
  4. Review Summarization
    • Summarize key points from reviews, focusing on main pros and cons.
  5. SNET Integration
    • Offer the service through the SNET marketplace.

Project details

In this section we describe how the system works by defining the following:

  1. Input Layer
  2. Processing layer
  3. Output layer
  4. System Architecture 
  5. Risk & Mitigation
  6. Potential revenue streams

A. Input

1. Product Name or Link

  • Description: The primary input provided by the user is the name of the product or a direct link to the product's page on an e-commerce platform.
  • Purpose: This information is used to identify the specific product that the user wants to analyze. It ensures that the tool fetches reviews relevant to the correct product.
Format: String
Example:
Product Name: "Samsung Galaxy S21"
Product Link: "https://www.amazon.com/Samsung-Galaxy-S21"
 

B. Processing

1. Query Analysis:

  • Description: The initial step in processing where the input product name or link is analyzed to extract relevant product details and features 
  • Function: This involves identifying the product category and relevant features. It ensures that the subsequent steps are tailored to the identified product.

2. Review Data Aggregation

  • Description: Raw review data fetched from various e-commerce platforms by the system based on the input product name or link.
  • Function: This step involves web scraping or API calls to gather all relevant reviews from specified platforms. It consolidates reviews into a single dataset for analysis.
  • The data consists of:
    • Review Text: The main content of the review.
    • Ratings: Numerical ratings given by the reviewers.
    • Metadata: Additional information such as review date, reviewer identity (if available), and helpfulness votes.
Example:
Format JSON Array
```json
    [
      {
        "platform": "Amazon",
        "review_text": "Great phone, but the battery life could be better.",
        "rating": 4
      },
      {
        "platform": "eBay",
        "review_text": "Excellent performance, worth every penny!",
        "rating": 5
      }
    ]
    ```
 

3. Fake and Bias Detection

  • Description: Analyzing the collected reviews to detect potential biases or fake content.
  • Function: This involves:
    • Bias Sentiment Analysis: Determining the overall sentiment (positive, negative, or neutral) of each review and use custimized LLMs (see TrustLevel Github) for bias detection.
    • Pattern Recognition: Identifying unusual patterns that may indicate fake reviews, such as multiple reviews with similar wording or reviews posted in quick succession.
    • Reviewer Analysis: Assessing the credibility of reviewers based on their review history and activity patterns.

4. Summary Generation

  • Description: Creating a concise summary of the main pros and cons extracted from the reviews.
  • Function: This step uses natural language processing (NLP) to:
    • Extract Key Points: Identify common themes and frequently mentioned aspects (e.g., quality, performance).
    • Generate Summaries: Compile the extracted information into a coherent summary that highlights the key pros and cons of the product.

C. Outputs

1. Balanced Review Summary

  • Description: A summary highlighting the main points from the reviews.
  • Content: 
    • Pros: Positive aspects of the product as mentioned in the reviews.
    • Cons: Negative aspects or common complaints about the product.
  • Purpose: Provides users with a quick overview of the most important information about the product.
Format: String
Example: "The Samsung Galaxy S21 is praised for its excellent performance and camera quality, though some users mention that the battery life could be better."

2. Adjusted Overall Rating

  • Description: A balanced overall rating for the product after accounting for and removing biases from individual reviews.
  • Calculation: 
    • Raw Rating: The average rating calculated from all collected reviews.
    • Adjusted Rating: A recalculated rating that excludes or downweights biased or fake reviews.
  • Purpose: Offers a more accurate representation of the product’s quality based on genuine user feedback.

3. List of Flagged Reviews

  • Description: A list of reviews identified as potentially biased or fake, along with reasons for flagging.
  • Content: 
    • Review Text: The content of the flagged review.
    • Flagging Reason: Specific reasons for identifying the review as biased, such as repetitive wording or suspicious review patterns.
  • Purpose: Helps users understand which reviews have been filtered out and why, promoting transparency in the analysis process.
Example:
Format JSON Array
    ```json
    [
      {
        "platform": "Amazon",
        "review_text": "Best phone ever! Buy it now!",
        "rating": 5,
        "flag_reason": "Overly positive"
      },
      {
        "platform": "eBay",
        "review_text": "Terrible product, don't waste your money.",
        "rating": 1,
        "flag_reason": "Overly negative"
      }
     ]

     ```

4. Bias Report

  • Description: An in-depth report on the biases detected in the reviews.
  • Content: 
    • Bias Analysis: Detailed explanation of the biases found in the reviews, including types and patterns of biases.
    • Reviewer Credibility: Analysis of reviewer credibility based on their activity and history.
    • Statistical Insights: Statistical data on the distribution of genuine vs. biased reviews.
  • Purpose: Provides users with comprehensive insights into the reliability of the reviews and the extent of detected biases.
Example:
Format JSON Object
    ```json
    {
      "platform_analysis": {
        "Amazon": {
          "total_reviews": 100,
          "flagged_reviews": 20,
          "bias_score": 0.2
        },
        "eBay": {
          "total_reviews": 50,
          "flagged_reviews": 10,
          "bias_score": 0.2
        }
      },
      "overall_bias_score": 0.2
    }

D. Technical Architecture

1. Data Collection Layer

  • Web Scrapers and APIs: This layer consists of web scrapers and will use APIs that aggregate reviews from various e-commerce platforms. Each scraper/API is designed to fetch review data (review text, ratings, metadata) in real-time or at scheduled intervals.
    • Tools: Python (BeautifulSoup, Scrapy), REST APIs.

2. Data Processing Layer

  • Query Analysis Module: This module identifies the product and relevant features from the input (product name or link) provided by the user.
    • Tools: NLP libraries (spaCy, NLTK), Customized LLM.
  • Review Aggregation Module: Aggregates reviews from different sources into a unified dataset.
    • Tools: Data integration frameworks, ETL (Extract, Transform, Load) processes.
  • Bias Detection Module: Utilizes sentiment analysis and pattern recognition algorithms to identify biased or fake reviews.
    • Tools: Customized LLM, TensorFlow, PyTorch.
  • Summary Generation Module: Generates concise summaries of the reviews, highlighting key pros and cons.
    • Tools: LLM (e.g. ChatGPT)

3. Integration and API Layer

  • API Gateway: Exposes APIs for external applications to interact with the review analyzer.
    • Tools: Node.js.
  • SNET Marketplace Integration: Ensures that the service is available on the SNET marketplace.

E. Potential Risks & Mitigation

1. Challenges with Data Collection

  • Risk: We might face some difficulties in scraping data from platforms with strong anti-scraping measures, such as CAPTCHA, IP blocking, and dynamic content loading.
  • Mitigation: Implement robust scraping techniques, use proxy services, and leverage APIs where available.

2. Bias Detection Accuracy

  • Risk: Developing bias detection and ensuring the accuracy and reliability of these algorithms is always connected with a risk that they will not work as desired. 
  • Mitigation: Our previous development for bias in news articles already works reliably for the detection of strong bias. A substantial part of the budget is reserved for continuously training and updating the model. 

F. Potential Revenue Streams

In order to finance the system in the long term, there are the following options for monetising the tool: 

  1. Subscription Model: Monthly or annual fees for premium features like advanced bias detection and detailed reports, targeting frequent shoppers and businesses.
  2. Pay-Per-Use Model: One-time fees for individual product analyses, appealing to casual users.
  3. Enterprise Licensing: Custom agreements for bulk access, aimed at e-commerce platforms and large businesses.
  4. Affiliate Partnerships: Integration with e-commerce sites, earning commissions per use or through revenue sharing.
  5. Data Insights and Reports: Selling detailed market analysis reports to businesses and analysts.

Competition and USPs

Some Competitors:

1. Fakespot: Detects fake and biased reviews using AI. 

  • Strengths: trusted, easy integration. 
  • Weaknesses: limited to detection.

2. ReviewMeta: Analyzes Amazon reviews for fake patterns. 

  • Strengths: user-friendly, free. 
  • Weaknesses: limited to Amazon, less advanced AI.

3. Trustpilot: Platform for user reviews with some AI analysis. 

  • Strengths: large user base, widely recognized. 
  • Weaknesses: susceptible to manipulation.

4. Yotpo: E-commerce marketing platform with review analytics. 

  • Strengths: full-featured, strong analytics. 
  • Weaknesses: marketing focus, not specialized in unbiased analysis.

 

Our Value Proposition

  1. Comprehensive Review Analysis: Aggregates reviews from multiple platforms, providing a holistic view of feedback.
  2. Advanced Bias Detection: Uses sophisticated AI for accurate detection of biased or fake reviews.
  3. Balanced Summarization: Summarizes key pros and cons, saving time and providing balanced insights.

 

Open Source Licensing

Apache License

Additional videos

Revenue Sharing Model

API Calls

API Revenue Service

1000

API Revenue Percentage

25

API Revenue Year

2026

Proposal Video

DF Spotlight Day - DFR4 - Dominik Tilman - AI-Powered Product Review Analyzer [by TrustLevel]

3 June 2024
  • Total Milestones

    7

  • Total Budget

    $78,000 USD

  • Last Updated

    3 Jun 2024

Milestone 1 - API Calls & Hostings

Description

This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.

Deliverables

You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.

Budget

$19,500 USD

Milestone 2 - Project Initiation

Description

1. Objective: Final definition of architecture and tools used. 2. Activities: This also includes updated market research on current market developments and which tools are new or most suitable. We will also take another look at the competition and see how they do it what we can learn from them and how we can do it better. 3. Calculation: 60 hours at 85 USD/h = 5100 USD + 2400 USD Licenses & Tools

Deliverables

Finalized system architecture document.

Budget

$7,500 USD

Milestone 3 - Input Layer - Review Aggregation

Description

1. Objective: Develop the input layer for collecting and normalizing reviews from various platforms. 2. Activities: - Integrate APIs with multiple e-commerce platforms. - Develop data extraction and normalization processes. - Implement a database for storing review data. 3. Calculation: 150 hours at 85 USD/h = 12750 USD

Deliverables

1. Functional Input Layer. 2. Code base shared through GitHub.

Budget

$12,750 USD

Milestone 4 - Processing Layer - Bias Detection Algorithms

Description

1. Objective: Implement the processing features including bias detection. 2. Activities: - Identify and flag biased or fake reviews. - Train costumed LLM for bias detection. - Implement NLP algorithms for sentiment analysis. 3. Calculation: 150 hours at 85 USD/h = 12750 USD

Deliverables

1. Functional Processing Layer. 2. Code base shared through GitHub.

Budget

$12,750 USD

Milestone 5 - Output Layer - Overall Rating Calculation

Description

1. Objective Development of the overall rating calculation. 2. Activities: - Development of the bias correction mechanism for overall rating calculation. - Compute a balanced overall rating after correcting for biases. - Generate concise summaries of reviews. - Deploy the system on TrustLevel Backend and on AWS for hosting. 3. Calculation: 150 hours at 85 USD/h = 12750 USD

Deliverables

1. Functional Output Layer. 2. Code base shared through GitHub.

Budget

$12,750 USD

Milestone 6 - Testing & Refinement

Description

1. Objective and Activities: Testing of the system and refinements. 2. Calculation: 50 hours at 85 USD/h = 4250 USD

Deliverables

Functional Product Review Analyzer Tool. ready to implement.

Budget

$4,250 USD

Milestone 7 - SNET Integration

Description

1. Objective: Integrate with the SingularityNET platform and publish the service on the marketplace. 2. Activities: - Implement SingularityNET API integration. - Publish the product on the SingularityNET marketplace. - Promote the service to attract initial users and gather feedback. 3. Calculation: 100 hours at 85 USD/h = 8500 USD

Deliverables

Integrated system Deployment scripts Marketplace listing

Budget

$8,500 USD

Join the Discussion (1)

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1 Comment
  • 0
    commentator-avatar
    Gombilla
    Jun 2, 2024 | 4:28 PM

    This will be a sought after solution and I would say the team did really well putting this up. But I also suggest that concerns may also arise regarding the potential for unintended consequences or biases in the overall rating calculation process, as well as the transparency of the methodology used to adjust ratings and identify biases.

Reviews & Rating

Sort by

8 ratings
  • 0
    user-icon
    mivh1892
    May 23, 2024 | 1:17 PM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    Combating Biased Reviews

    The "AI-Powered Enhanced Product Review Analyzer" project has the potential to make a positive impact on the e-commerce market and benefit both consumers and businesses.

    1. Feasibility:

    • Clear goals: The project aims to develop an AI tool that helps consumers and businesses make more objective and reliable product evaluations.
    • Viable solution: The project proposes a specific technical solution that includes data collection, data processing, integration, and API layers.
    • Experienced team: The development team has experience in the field of bias detection and has created a proof of concept (POC) for the project.
    • Available resources: The team has the necessary resources available, such as a development framework and experience integrating with SNET.

    2. Sustainability:

    • Market demand: The demand for reliable product review tools is growing due to the popularity of e-commerce.
    • Competition: The project has a competitive advantage over other competitors due to its combination of advanced AI and the ability to aggregate reviews from multiple platforms.
    • Revenue model: The project proposes potential revenue models such as subscriptions, pay-per-use, enterprise licensing, etc.

    3. Desirability:

    • Positive impact: The project can help consumers make more informed purchasing decisions and protect them from biased reviews.
    • Benefits for businesses: The project can help businesses build trust with customers and improve brand reputation.
    • Community interest: The project has the potential to attract interest from the AI community and the e-commerce community.

    4. Usefulness:

    • Comprehensiveness: The project provides features such as review aggregation, bias detection, summarization, and detailed reporting.
    • Accuracy: The project uses advanced AI to ensure high accuracy in bias detection and product evaluation.
    • Ease of use: The project provides an API and a user-friendly interface for easy access and use by users.

  • 0
    user-icon
    TrucTrixie
    Jun 9, 2024 | 2:11 PM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    Benefit from multi-functional uses

    I see the comprehensive impact, or in other words, the rather comprehensive benefits that this proposal brings as a product of the proposal to provide multiple features such as error detection, collection of assessments with detailed reports to the owner. I really like this useful impact and hope it will be implemented into practice soon.

  • 0
    user-icon
    Max1524
    Jun 9, 2024 | 1:02 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 4
    • Usefulness 5
    Reveal the identities of the remaining 4 members

    In the blockchain world, no one is familiar with Dominik Tilman. He has quite a reputation in the community. That's why even though Dominik Tilman hasn't fully disclosed the identities of the remaining 4 members of the team, I still have quite a bit of confidence in this team - I still give advice to Dominik to fully reveal his identity as soon as possible. all team members to gain complete trust from the community. Wishing Dominik's proposal success soon.

  • 0
    user-icon
    Tu Nguyen
    May 23, 2024 | 4:26 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 4
    • Usefulness 5
    AI-Powered Product Review Analyzer

    The problem they will address is that the credibility of product reviews is often affected by biased or fake reviews. This is quite a common problem in practice. Manipulated reviews will influence consumers' purchasing decisions. The solution of this proposal: they will develop an MVP version of the AI-powered Product Review Analyzer, which will be integrated into SingularityNET AI Marketplace to provide comprehensive product reviews representative and objective for consumers and businesses. Hope they will complete this solution well. Another positive point is that they research their competitors quite clearly. Project members have a wide range of experience and relevant skills. The milestones are quite detailed. I just have a little advice: they should determine the start and end times of the milestones.

  • 0
    user-icon
    BlackCoffee
    Jun 10, 2024 | 1:06 AM

    Overall

    5

    • Feasibility 4
    • Viability 5
    • Desirabilty 4
    • Usefulness 5
    The superiority of AI

    The goal of the proposal is clearly defined by the author when aiming for an AI integration tool to help consumers/businesses make more reliable assessments of selected products. I appreciate this goal for its usefulness, because it is bringing direct benefits to consumers/businesses. It's true that AI superiority is really good.

  • 0
    user-icon
    Joseph Gastoni
    May 23, 2024 | 9:05 AM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    This proposal outlines TrustLevel\'s AI tool

    This proposal outlines TrustLevel's AI tool for analyzing product reviews and generating unbiased ratings. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • High: The concept is feasible, leveraging existing techniques like web scraping, sentiment analysis, and natural language processing (NLP).
      • Strengths: Building on existing technologies simplifies development.
      • Weaknesses: Data scraping can be challenging due to anti-scraping measures by e-commerce platforms. Bias detection requires careful algorithm development and training to ensure accuracy.

    Viability:

    • Moderate: Success depends on market adoption, data access, and the effectiveness of bias detection algorithms.
      • Strengths: The proposal addresses a growing concern about fake and biased reviews. The SNET marketplace integration provides reach.
      • Weaknesses: The proposal lacks details on the cost of the service (if any) and its competitive advantage. Relying solely on scraping for data collection might be limiting.

    Desirability:

    • Moderate-High: For consumers and businesses concerned about review authenticity, this could be desirable.
      • Strengths: The proposal offers a tool to identify potentially biased reviews and provides a more balanced overall rating.
      • Weaknesses: The proposal needs to clearly explain the value proposition for different user groups (consumers vs. businesses).

    Usefulness:

    • Moderate-High: The project has the potential to improve the reliability of online reviews, but its impact depends on the accuracy of bias detection, data comprehensiveness, and user adoption.
      • Strengths: The proposal offers a way to analyze reviews and generate summaries highlighting key points.
      • Weaknesses: The proposal lacks details on how the tool ensures the comprehensiveness of review data and how it handles situations with limited reviews or niche products.

    Overall, TrustLevel's proposal has a valuable concept, but focus on:

    • Data Collection Strategy: Develop a robust approach to collect review data beyond scraping, considering API access and partnerships with e-commerce platforms.
    • Bias Detection Accuracy: Clearly define how the bias detection algorithms are developed, trained, and validated to ensure they are reliable and unbiased themselves.
    • Competitive Advantage: Clearly differentiate TrustLevel's tool from existing review analysis platforms and emphasize its unique features (e.g., focus on bias detection).

    Strengths:

    • Addresses a growing concern about fake and biased reviews.
    • Leverages NLP and AI for review analysis and summarization.
    • Integrates with the SNET marketplace for wider reach.

    Weaknesses:

    • Data collection strategy relies on scraping which can be unreliable.
    • Accuracy of bias detection algorithms requires careful development and validation.
    • Needs a clear value proposition and competitive advantage.

    By addressing these considerations, TrustLevel can develop a valuable tool for users to navigate the world of online reviews with more confidence.

  • 0
    user-icon
    Nicolad2008
    Jun 8, 2024 | 3:37 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Product reviews

    This tool promises to bring a more balanced and accurate view to consumers by collecting evaluation from many e -commerce platforms and using AI to detect, eliminate bias and manipulation.
    The project has been developed based on the concept (POC) and model training frames, showing high ability to implement and valuable experience has been accumulated. This clearly shows the potential of the tool in helping consumers make smarter shopping decisions and support businesses to manage prestige products. However, I also realize that the project may be difficult due to the variety and complexity of evaluation data, as well as maintaining AI's accuracy and updates in the e -commerce environment that changes rapidly. quickly. Ensuring transparency and fairness in the analysis process is also an important factor to be considered.

  • 0
    user-icon
    CLEMENT
    Jun 2, 2024 | 4:34 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Enhances trust in the online shopping experience

    As an advocate for product quality, I believe this project has the potential to make a significant impact by providing consumers and businesses with unbiased, comprehensive product reviews.

    I will also comment that this project will enhance transparency and trust in the online shopping experience, ultimately leading to more informed purchasing decisions and improved customer satisfaction. 

    As a contribution to the SingularityNET AI community, this AI-Powered Product Review Analyzer provides a valuable tool for both consumers and businesses seeking reliable product review ultimately contributing to the growth and expansion of the SNET ecosystem. 

Summary

Overall Community

4.8

from 8 reviews
  • 5
    6
  • 4
    2
  • 3
    0
  • 2
    0
  • 1
    0

Feasibility

4.6

from 8 reviews

Viability

4.4

from 8 reviews

Desirabilty

4.4

from 8 reviews

Usefulness

4.8

from 8 reviews

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