Advanced AI Sentiment Analysis: Conversational Context

NickArnett |
 03/08/19 |
3 min read

Artificial intelligence (AI) may not have taken over the world just yet – but soon enough it will become seamlessly integrated into our everyday lives. Consumers have already embraced aspects of AI without realizing it, or have experienced frustration with fledgling versions of the tech as it’s available now.

The challenge with today’s AI technology comes down to one missing ingredient – but it’s a big one. Here’s how brands can be ahead of the game as AI evolves.

Context Is the Secret Sauce

When it comes to sentiment analysis, AI technology is a powerful advantage. Combining the methods of expert systems, machine learning, and deep learning, Natural Language Processing (NLP) becomes faster and more accurate, allowing brands to understand consumer emotions like love and hate – and even more nuanced emotions like skepticism or disinterest.

What makes sentiment analysis so valuable is the way it contextualizes social conversations. Instead of a list of topics consumers are discussing, you have a weighted list – one that clearly defines the topics that matter most, as well as why they’re important to the people talking about them.

A look at sentiment drivers for BMW and Mercedes

This allows brands to act with confidence, instead of wasting time on trial and error.

But there’s an even bigger advantage on the horizon.

The Next Phase of AI Evolution

As AI continues to evolve, one of the biggest hurdles is instilling conversational context into this technology – so it can respond in a more human way to human interactions.

Think about the AI technology currently assimilated into our lives – in the form of voice assistants and customer service chatbots, for example. Though there’s a lot to like about these technological wonders, they aren’t without their flaws.

Voice assistants like Siri or Amazon Echo can answer specific questions based on their programming and interfaces. You ask for the day’s weather, and it connects to whatever weather app it is programmed to. You ask it to remind you to pick up your child after school, and it interfaces with your calendar app to set an alert.

But you can’t ask Siri for an opinion (among other things) – because “she” can’t extrapolate context well enough to provide such information.

Similarly, chatbots feel like a bit of science fiction come to life – until you hit a roadblock because the bot can’t process more nuanced requests. Consumers and brands both end up frustrated, blaming AI for not being up to their expectations.

But in reality, it’s a human problem. These failures, and the need for context are on the minds of computer scientists and all those working in AI development.

In sentiment analysis, the data exposes human emotions because humans have instilled the programming with all the nuances of human language – national languages, regional dialects, slang, pop culture terms, abbreviations, sarcasm, emojis, etc. And NLP engines – with the help of AI technology – are designed to parse text, and even images now, to put human emotions into context for brands.

This allows for highly accurate, contextualized insights around what consumers think, feel, want, and, importantly – don’t want.

Human-computer-interactions (HCI) are at their heart human-to-human interactions, transferred via a third party – i.e., the computer, chatbot, Siri, etc. The more AI technology brings emotion into the equation, the better the devices it powers will understand context and be able to relate to humans in the way they want.

You Can Have a Head Start

And here’s that advantage we mentioned earlier:

At NetBase, we already have contextual capabilities in place, and we are perfectly positioned to keep pace with AI advances as they happen. As use of AI devices extends beyond the social sphere we’re familiar with today, we’ll be able to bring that data into the mix as it evolves, offering the same precise, emotional insights we surface on social today.

And our customers will be well-versed in their understanding of sentiment-based data – putting them ahead of brands that sat too long on the fence and have to play catch-up.

Providing this edge is why we’ve always focused on innovation as a core business practice. It’s why we’re using AI Analytics in ways that other social analytics providers simply aren’t. We watch trends and technological advances closely and know what’s coming – and we can’t wait to be the first to bring these opportunities to you!

Ready to get in on the ground floor of the AI revolution? Talk to us today about our AI Analytics tools and how they can take your brand into the future.


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