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Solving the Sentiment Data Challenge

Data Science and Technology

By Christian Cifelli  |  December 8, 2020

As the amount of readily available, non-traditional financial data on companies continues to grow, it is impossible for a human being to reasonably sift through and digest each piece of information.

However, the value of textual information surrounding a company is undeniable, and failure to incorporate this intelligence into a model or research on a company puts investors at a disadvantage. The primary solution to ingesting this never-ending stream of data, specifically when it comes to text and audio, is known as Natural Language Processing (NLP).

NLP, which is the foundation of language-based sentiment, can be defined as the process to systematically categorize the attitudes or emotions found within textual information. In the financial world, this categorization is taken a step further by using the output to predict the future performance of an asset. Furthermore, programmatic audio analysis can be used to judge sentiment based on a speaker’s tone, manner of speaking, pauses, and more.

What is Sentiment Analysis?

Sentiment analysis data primarily takes two forms: raw unfiltered text or audio, or derived work. The first decision a user will make is whether to perform their own sentiment analysis or invest in a third-party sentiment vendor to generate output or sentiment scores for them. Regardless of their needs, there are multiple choices when it comes to whether to buy, build, or create a blended solution.

Raw data comes from a variety of sources, and being able to process this type of data requires deep expertise. An end-user will have to program an algorithm to parse through the content in such a way that detects the source’s attitude and emotion and catches things that may not be apparent to a human digesting the information.

The difficulty of this task has opened the door for sentiment vendors who can perform this analysis and replace the need for in-house expertise. Each differentiates itself based on qualities like source and granularity and provides a variety of solutions in the space to suit the needs of a broad spectrum of users. However, both the solutions and vendors in the sentiment space offer a variety of advantages and disadvantages.

Understanding Investment Applications of Sentiment Analysis

Intuitively, performance is correlated to sentiment surrounding a given asset, whether that be a stock, commodity, or interest rate.

The challenge lies in identifying, processing, and analyzing sources that provide predictive insight into the sentiment of the asset. The source of sentiment information—along with the desire to build, buy, or blend metrics—will inform how the data is used.

When considering the various investment applications of sentiment data, it makes sense to align the use cases with the source of the data. Different sources inherently provide data that is best suited for different users and applications.

For instance, central bank communication is more adept at providing insight into the treasury market of a given country, whereas the sentiment of a 10-K’s Management Discussion and Analysis section is going to be very company-centric.

As vendors typically only supply sentiment data from one type of source, it’s important to understand the merits of each, the challenges they pose, and the value they add to financial analysis.

Read more in the eBook, Solving the Sentiment Data Challenge: A Conclusive Guide for Leveraging Connected Content to Facilitate Sentiment Vendor Selection, Evaluation, and Data Integration.


Christian Cifelli

VP, Content & Technology Strategy

Mr. Christian Cifelli is Vice President, Product Strategy within the Content and Technology Solutions group at FactSet. In this role, he focuses on developing an in-depth understanding of our clients' content and integration needs, identifying market trends, and serving as a subject matter expert to inform decisions and define the content pipeline for Open:FactSet. Mr. Cifelli earned a B.S. in Finance from Villanova University.