Background

Digital Alpha's solution helps investment firms cut through the noise and extract valuable insights from Twitter data. Developed using a combination of AWS services and algorithms, this solution uses sentiment analysis to determine the polarity of specified text at the document, sentence, or aspect level. The web scraping algorithm collects top tweets based on specific keywords, companies, and industry expert handles and stores the data in an S3 bucket. The pre-processing algorithm removes acronyms, emoticons, and other unnecessary information, ensuring that the emotions conveyed in the tweets are correctly represented. The solution is highly scalable and can handle large amounts of data thanks to the power of AWS services. The cleaned tweet data is analyzed to generate social sentiment indexes, which are stored in an Athena table for easy querying and analysis.

Features

• Generate real-time insights from keyword specific tweets
• Compare sentiments over different time period
• Integrate into the existing investment analyses platforms/tools

Benefits

• Speed & Efficiency
• Simplicity
• Cost
• Data Democratization
• Real-time Data Access

Common Use Cases

• Sentiment portfolio analysis on specific sector
• Monitoring of key people at the underlying funds by asset allocators
• Social sentiment on portfolio companies

Deliverables

• Design & customizations for specific client needs
• Implementation
• Deployment