Learn how Amenity Analytics’ NLP expertly handles the nuances and sophistication of human language. Amenity's unparalleled accuracy helps businesses make better decisions.
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.
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.
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.
Sifting through management’s earnings commentary for key insights presents its own challenges as the lack of clarity from executives and the noise from all things Coronavirus makes it easy to overplay or underplay a decision. Expedia and Hilton Worldwide Holdings are two recent examples of how NLP (done right) can be a successful anticipatory tool with regards to a company’s fundamentals in a volatile market.
It’s no surprise that Zoom's revenue and cash generation growth has been stunning, but when we view the latest earnings through our NLP Insights Platform it returned surprising scores in terms of overall sentiment, margin and in particular, guidance which show a downward trend, so we explore further.
Here are 5 examples of how corporations and investors can use Amenity's NLP to systematically understand how ESG factors in the news drive company performance, with an unbiased dataset built around transparency and granularity.
ESG is one of many factors when it comes to assessing credit ratings. However, the increasing relevance of environmental and social issues warrants treating ESG as a core factor in credit analysis through the use of NLP.
In a follow-up to our previous Restaurant sector write-up we identify off-premise sales growth as an interesting variable to track. Takeout and delivery channels have been the primary focus for most restaurant companies as they attempt to minimize cash burn rates. BJ’s Restaurants (ticker: BJRI) is one interesting case study we explore in detail.
Executives from Intel and GenTrust join Amenity Analytics CEO Nate Storch to discuss the latest challenges and opportunities surrounding ESG.
We leveraged our NLP model to create the Amenity Coronavirus Tracker dashboard – a live feed which searches for relevant insights into the virus’ investment implications contained within news sources and earnings call transcripts. We highlight findings impacting retail, food delivery and teleconferencing.
Keep up to date with our analyses and how we're making changes.