Alpha Research: Analyzing Sentiment Data Using Bloomberg and Python

Market sentiment is one of the most important drivers of asset prices. Sentiment analysis helps investors gauge the market’s mood by analyzing news, social media, and other public sources of information. Bloomberg provides rich data on financial markets and news, which can be leveraged to perform sentiment analysis and inform trading decisions.

This article will focus on how to use Bloomberg’s sentiment data for market analysis, particularly for stocks or sectors, and how to integrate it with Python to develop a framework for sentiment-driven trading strategies.

What is Bloomberg Sentiment Data?

Bloomberg provides sentiment data derived from news articles, social media, and other public sources using natural language processing (NLP) algorithms. This data reflects the tone of coverage (positive, negative, or neutral) around a particular stock, sector, or market event. Bloomberg’s sentiment analysis can help traders and investors quickly assess the market’s attitude toward an asset without manually reviewing thousands of articles.

Bloomberg’s Terminal offers real-time sentiment data across the financial market spectrum, providing insights into:

• Earnings Reports Sentiment