What Is Thematic Sentiment?

Thematic sentiment analysis is the process of identifying and categorizing feelings, opinions, and attitudes found within text documents. AI advancements has led to major improvements in the quality of sentiment data and the ways in which this data can be applied. Organizations are using thematic sentiment to evaluate and assess everything from SEC filings and earnings call transcripts to corporate documents and internal communications to social media, news articles, and online forums.

Thematic Sentiment vs Sentiment Analysis

Sentiment analysis, also known as "opinion mining", gauges the predominant opinion toward a subject of interest (commonly referred to as an“entity”) such as people, places, organizations, locations, and things. Opinions may be classified as positive, negative, or neutral.

Thematic analysis provides a structured, systematic approach to understanding sentiment. Viewing sentiment data grouped by thematic events — for example,commentary on a product launch or a company’s growth forecast — makes it possible to spot patterns, trends, and outliers, and track these findings over time.

Why the Interest in Thematic Sentiment?

Organizations today find themselves having to access seemingly endless streams of text data: internal and external reports, online posts, transcripts,reviews, emails, blogs, news articles, and so forth.

While there are valuable nuggets of information waiting to be revealed within these data sources, the process of sifting, sorting and analyzing all of this information is no small feat. Such an endeavor requires a significant outlay of time and money, not to mention the knowledge of a domain expert to interpret the data correctly.

So how do organizations negotiate these obstacles? By using next-gen AI solutions that can extract usable insights from any text data source at scale,while also displaying a sophisticated understanding of human language.

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