BlogAnalyzr

Short description
BlogAnalyzr is designed to analyze the qualityof blog posts of technical writers using CM for necessary offchain analysis. The more point an author's work gets, the higher he gets in ranks.

Motivation behind the project

The inspiration for this project stems from the growing need within the tech community to identify and recognize high-quality content creators. With the vast amount of technical content available online, it can often be challenging for readers to discern which creators consistently produce valuable and informative content.

To address this challenge, our project leverages advanced text analysis techniques, sentiment analysis, and readability assessment to evaluate the quality of blog posts authored by various creators. By automating the process of analyzing content, our platform aims to provide users with objective insights into the expertise and effectiveness of different content creators.

Furthermore, our integration with Cartesi Machine introduces a decentralized and trustless mechanism for rating content creators without breaking the bank as compared when using traditional EVM. By utilizing blockchain technology, we ensure transparency, immutability, and integrity in the rating process, thereby fostering a community-driven approach to content evaluation.

Overall, our project seeks to empower readers in the tech community to make informed decisions about which creators to follow, ultimately fostering knowledge sharing, collaboration, and innovation within the industry.

Detailed description
BlogAnalyzr is designed to analyze the quality of blog posts of technical writers using the Cartesi Machine for necessary offchain analysis. It utilizes Puppeteer for web scraping, Natural and Sentiment for sentiment analysis and readability scoring, and provides functionality to rate creators based on the quality of their posts.

Features

• Analyze the quality of blog posts by extracting text content, performing sentiment analysis, and calculating readability score
• Rate creators based on the average sentiment score and readability of their posts
• Provable offchain blog sentiment analysis and readability scoring.
• Flexible and customizable sentiment analysis using both built-in lexicons and custom sentiment labels.
  
  
  

Stacks

Cartesi Machine: For provable off-chain sentiments analysis calculations. 
Contentful: Headless CMS management flatform for content
Nextjs: Frontend 

Challenges faced
Getting a realistic sentiment and readability score for authors was a hassle till I had to generate a sentiment lexicon tailored to ICT and software engineering terms and expressions. This helped in narrowing it down to the niche the platform was meant for.

Had to jettison some libraries at some point as it was not allowing docker to copy the built image due device resources I guess.