Twitter Sentiment Scraper
Python, Tweepy/Snscrape
This project implements an automated system that collects over 30,000 tweets to facilitate comprehensive sentiment analysis. By efficiently gathering and structuring vast amounts of social media data, it delivers a clean and optimized dataset designed to analyze brand perception accurately. This enables businesses to monitor public sentiment, identify trends, and make data-driven decisions for marketing and reputation management.
Key Metrics
95%
Customer Satisfaction
<2s
Response Time
50K+
Queries Handled
95%
Accuracy Rate
Technologies Used
Python
Tweepy
Snscrape
Results & Impact
Reduced customer wait times by 80%
Improved customer satisfaction scores by 35%
Decreased operational costs by 60%
Handled 95% of queries without human intervention