Approach & Timeline:
Day 1-3: Setup Twitter API, collect data sources (whitepapers, blogs, etc.), implement NLP summarization.
Day 4-5: Develop Q&A feature using NLP, integrate ecosystem docs and FAQ sources.
Day 6-7: Implement trend analysis and sentiment evaluation, deploy bot, and test for accuracy.
Core Features Implementation:
Summarization:
Leverage web scraping (using BeautifulSoup or Scrapy) to pull content from whitepapers, podcast transcripts, Medium blogs, GitHub updates, and Twitter threads.
Use NLP models like spaCy or Transformers to summarize key points (e.g., collaborations, token launches, dates, alpha insights).
Schedule a task every 12 hours to auto-publish the summarized content via Twitter API.
Interactive Q&A:
Implement a simple NLP-based question answering system using OpenAI GPT-3/4 or Rasa to process and respond to user queries by referencing ecosystem documentation and community threads.
Monitor Twitter mentions, process questions, and reply with relevant data, enhancing user engagement.
Insights & Trend Analysis:
Use Twitter API to track ecosystem-related hashtags and threads.
Implement sentiment analysis with TextBlob or VADER to evaluate public opinion on specific topics like DeFi protocols.
Set up alerts for trending keywords or significant updates.