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Article • 11/12/2023

Beyond boolean: Why Fortune 500s lean on AI-powered search to make sense of the world

It didn’t take long after Google first went live in 1998 for the company name to become a verb. For more than two decades, “Google it” has been shorthand for looking up information on the internet and getting to the bottom of any question. But as any communications professional can tell you, “Googling it” isn’t always a direct path to guaranteed results. 

Search engines like Google, as well as traditional media monitoring tools rely on boolean search logic. For most consumers, boolean works just fine. But for PR and comms professionals tasked with generating an exhaustive scan of the media landscape, boolean logic demands complex search strings and the use of “and,” “or,” and “not” operators to get pertinent results. Navigating the hundreds of billions of websites indexed by Google requires the ability to craft precise search queries — a complex and time-consuming process that is easy to get wrong and almost impossible to scale. In even the best situations, organizing Google search results into useful data that tells a story requires hours of manual tagging, sifting through endless false positives, and filtering out irrelevant stories. “Googling it” no longer cuts it.

Companies today need to be able to understand the media landscape around their brand quickly, easily, and precisely. That’s where AI excels. Each day, Signal AI’s sophisticated AI-powered search engine, known as AIQ, ingests more than five million documents (including all kinds of content: traditional news, podcasts, blogs, social media, and more) in 75 languages from more than 150 markets. That content is then analyzed and organized into searchable, editable concepts, such as “Topics” (ex: Sustainability or Automation) or “Entities” (ex: Organizations, People, or Locations), providing an immediate and thorough overview of the media landscape. Letting AI do this work gives PR and comms professionals a way to instantly grasp how their brands are being discussed, freeing them from the drudgery of all that manual labor enabling them to get to work putting that data to action — a much better way to spend time and resources. 

Search with context built-in

One of the greatest advantages of AIQ-powered search over keyword-based Boolean search is its ability to offer precise analysis that takes into account the context of a passage or piece of content. Boolean search, on the other hand, is binary. Either the searched phrase exists or it doesn’t. There’s no concept of context; all words are treated equally and individually, as if in a silo. This means the way things are said is not taken into account. Colloquialisms are not understood and things like emotions, slang, and sarcasm cannot be considered. But context is crucial, in more ways than one. 

This is particularly true when it comes to measuring brand sentiment. Most sentiment models available today use simplistic techniques that analyze individual words of a sentence in isolation, averaging them to categorize the overall sentiment of a piece of content as positive, neutral, or negative. But this is flawed. Imagine if a review of a new smartphone said the brand “absolutely killed it with its new design!” In isolation, “killed” could be read as a negative word. Signal AI’s sentiment engine, on the other hand, is “entity-based,” meaning it looks at an entire section of text and understands the target of the statement before labeling its sentiment.

Media monitoring is another example of the importance of context-aware search. Consider the payment processing company Square and how a boolean search isn’t able to distinguish between content discussing Square the business from content discussing square as a shape. Signal AI can, and it’s even able to stay up to date. When Square changed its name to Block, the platform didn’t skip a beat. 

Narrative white spacing with AI 

Getting your brand on the radar of the right people for the right reasons is an increasingly difficult task. Signal AI’s tools can help identify “reputational white spaces” — topic areas that afford brands the opportunity to drive reputational gains by being associated with positive conversations that have little competition, increasing the odds that related brand coverage is positive and quick to accelerate. The Signal AI 500, a global reputation ranking of 500 of the world’s most talked-about companies, is a powerful tool to help businesses identify these white space opportunities before competitors. 

AI can also help brands understand and quantify how sentiment and perception evolve over time. Signal AI’s tools can identify shifts and patterns in public perception and present the information in easy-to-digest graphs and charts. Providing stakeholders with an easy way to understand the evolution of their brand’s perception year over year or month over month helps enable strategic, data-informed decision making.

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