May 29, 2023
No image
Completed
B2C: 16% Conversion from Real Time Twitter Sentiment Analysis
$25,000+
Less 1 month
United Kingdom
2-5
Service categories
Service Lines
Artificial Intelligence
Big Data
Domain focus
Media & Entertainment
Subcategories
Artificial Intelligence
Machine Learning
Big Data
Data Analytics
Challenge
Social sentiment moves at the speed of social media – streaming by the second. During COVID-19 the world came closer together, yet opinions were divided online.
The client wanted to gauge social sentiment on COVID-19 to provide data-driven insights to help align product offerings to audiences segmented by sentiment and create targeted result-driven campaigns.
Social sentiment moves at the speed of social media – streaming by the second. During COVID-19 the world came closer together, yet opinions were divided online.
The client wanted to gauge social sentiment on COVID-19 to provide data-driven insights to help align product offerings to audiences segmented by sentiment and create targeted result-driven campaigns.
Solution
Vacon created a dashboard showing real-time (hourly) analytics on COVID tweets and related sentiment. The real-time dashboard showed the number of positive, neutral, and negative COVID tweets upon the hour.
Word cloud sentiments showcased dominant words being used on Twitter.
How it works:
The Python-based script scans Twitter every hour, scraping tweets mentioning COVID.
The tweet data was cleaned from stop words and other text, including emojis.
After data cleaning, the text classifier was built using tf-idf methods, in combination with an SVM Classifier.
Once the training was complete, an API was built to report the result on live tweets to a Tableau dashboard.
Vacon created a dashboard showing real-time (hourly) analytics on COVID tweets and related sentiment. The real-time dashboard showed the number of positive, neutral, and negative COVID tweets upon the hour.
Word cloud sentiments showcased dominant words being used on Twitter.
How it works:
The Python-based script scans Twitter every hour, scraping tweets mentioning COVID.
The tweet data was cleaned from stop words and other text, including emojis.
After data cleaning, the text classifier was built using tf-idf methods, in combination with an SVM Classifier.
Once the training was complete, an API was built to report the result on live tweets to a Tableau dashboard.
Results
Witnessed a remarkable 37% reduction in customer acquisition costs (CAC)
Elevated their success with a substantial 16% boost in conversions
Propelled the online presence with an impressive 27% increase in click-through rates (CTR)
Witnessed a remarkable 37% reduction in customer acquisition costs (CAC)
Elevated their success with a substantial 16% boost in conversions
Propelled the online presence with an impressive 27% increase in click-through rates (CTR)