Event Detection: Analytics Becoming More Personal

Sentiment Analysis is a hot technology at the moment. Marketers are interested in the perception that consumers have about  a specific brand, product or service as this is found in unstructured text. Some people claim that Sentiment Analysis does not meet their expectations but also that it is not straightforward for a company to find the “right” solution.  Comparing different Sentiment Analysis solutions could prove a difficult task.

Marketers and Decision makers need insights with which they can make better decisions – They need both Reports and Intelligence. Therefore the question that always follows the finding that “Your product has a 35% negative sentiment in the past 10 days” is “Why”.  Social Media Monitoring tools must also  provide actionable Intelligence. 

All this is important information as it shows why your Brand / Product / Service could be losing customers. You monitor what is being said, identify whether a negative or positive Sentiment Trend is declining or rising and take necessary actions accordingly.

One of the questions i often get is what other applications can emerge from using Text Analytics and Data Mining. With Text Analytics and Data Mining we can find behavior patterns on many levels and -assuming that information such as Tweets will keep coming- the understanding of consumers can  go to the next -and sometimes more personal- level.

One of these applications is Event Detection. I am not aware if Event Detection is provided by any tool at the moment but i believe that this type of analysis could become a next major source of consumer insights. But what exactly is “Event Detection”?

Since we are able to have a computer automatically identify whether a phrase contains positive, negative or neutral sentiment, perhaps we could use Text Analytics and Machine Learning to detect that a specific event has occurred to an individual from the Tweets that someone posted such as “i’ve just returned from holidays”. But that’s not all. We can mine for patterns of consumer behavior given the fact that an event has occurred. And that potential knowledge from such an analysis could be very powerful. Because apart from the emotions that a product / service / person generates, the same applies for events happening in our lives. These events and the emotions they create can sometimes change our lives and also drive our decisions. A logical next step is to collect several behavioral Data and use Data Mining to analyze this information.

I will discuss an example of using Event Detection towards the end of my presentation on the 7th Annual Text Analytics Summit in Boston this May along with the reasons for such an analysis being important and i am looking forward to the reactions.

The fact is that with more insights, privacy issues arise even more and I get an increasing number of people asking me about privacy. I was also interviewed by a major British newspaper last month on what companies can learn by applying “Super Crunching” on Tweets. I tried to show both worlds of “Super Crunching” but the truth is that consumer insights become more personal as companies understand the value of structured and (more recently) unstructured information.