This is the third and final part in my Engaged Web series of blog posts.  If you missed the previous posts, you can access them using the links below;

Part I: The Engaged Web, Part II: Speed is the New Currency

3 key challenges

There are 3 key challenges which organizations face when measuring engaged two-way media:

1. Sentiment is fluid and can change rapidly online. What begins as positive can change to negative and even back to positive. In other words, it evolves. It’s crucial, therefore, to look at trends and movement rather than just volume.
2. Defining what is positive or negative is based upon your point of view. What good for one organisation is not necessarily good for another. Perspective is paramount.
3. The engaged web has its own language. The syntax used on Facebook and Twitter is very different to that of conventional prose. Think hash tags, emoticons etc.

In my view, the measurement industry needs to move from looking at ‘what has happened’ to ‘why it has happened’. But, as we see above, there remain some real challenges.  Effectively measuring reputation requires measuring all reputational influence.  We must measure traditional media (newspapers, magazines etc) alongside the Facebooks and Twitters of the world. If we don’t, we will fail to understand trends and patterns and establish the true connections.

The Case for Automated Sentiment Analysis

So how does an organisation know at the right time what is being said about it across tens or hundreds of thousands of media channels? It simply cannot be achieved quickly enough (let alone cost-effectively) with human evaluation methodologies. This is why more and more companies are turning to automated sentiment analysis.

Sentiment analysis engines have traditionally used a dictionary-based approach to measuring and identifying sentiment.  This method needs a dictionary of at least 250,000 words to be anywhere near effective.  It also means that it must be constantly updated with new words if it is to stay on top of the latest linguistic nuances.

However, superior tools are now being developed which operate around rule-based methodologies that do not use dictionaries, but instead analyze grammar and context.  This allows them to have a far greater level of language independence plus the ability to cope with slang and the other syntax challenges previously mentioned.  Tools which use this approach also have the ability to self learn and automatically adapt to language change as it happens which is critical when measuring the constantly-evolving engaged web.

Over the years, automated sentiment analysis has had a few false horizons and there is, quite justifiably, cynicism from some as to its efficacy. But this is now being cracked. Huge gains are being made in accuracy, speed and usability.  As this develops, the world will become divided into those who great reputation management software and those who don’t.  There will be clear winners and losers. The winners will be using sentiment analysis platforms to elevate the human role to high level analysis and decision making, while those who don’t will be left drowning in thousands of pages of posts and tweets, wondering where it all went wrong.

Sam