Overview

The client runs a website promoting knowledge-sharing through user-generated content. Users ask questions and connect with insightful contributors; featured answers are used in blogs. We implemented advanced technology to filter out inappropriate content. The project utilized deep learning for natural language processing, focusing on semantic object correspondence.

Technical Stack

  • AWS Lamda
  • Python
  • Keras
  • React
  • Postgress SQL
  • Industry

    Education

  • region
  • Region

    USA

  • project-size
  • Project Size

    Non- Disclosable

Highlights

AI Chatterbots

Sentiment Analysis

Cloud Infrastructure

Serverless Model Deployment

Challenges & Solutions

Develop a process to identify potential inappropriate messages and train the detector.

  • Solution: We implemented machine learning and manual review models to detect and mark insincere questions. The models were trained on comprehensive data, including identified phony questions.

The intent was to address toxic and disruptive content on the website.

  • Solution: We developed scalable methods to detect toxic and misleading content, incorporating a combination of machine learning models and a prediction module deployed in a serverless environment.

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The Development

  • Deep Learning Architectures Implemented
  • Content Recommendation System
  • Controversial Event Extraction
  • Comprehensive Training Data
  • no.-of-resources
  • No. of Developers

    04

  • time-frame
  • Time Frame

    December 2021 - Ongoing

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