Sentiment Analysis and Your Reputable Brand

Paula HingleyEducational

Human emotion is complex. It’s one of the seemingly few things that computers haven’t quite figured out how to navigate yet. So when we see a social media platform offering sentiment analysis as a feature, what does that really mean?

Here at Echosec, we help organizations find and filter billions of social media posts every day. When we zoom in on an area of interest, we grasp the entire post context. Echosec can get the location, the time & date of the post, there are images associated with much of the text, and (most importantly) we’re human beings. We are much better than computers at detecting nuances like sarcasm. With these advantages, the overwhelming sentiment of people in that location is often very obvious.

For example, the social sentiment from a luxury hotel in the Bahamas is generally quite positive. The sentiment from the infamous music festival flop #FyreFestival this April, was quite the opposite.

This kind of analysis is a bit different from the current definition, and we’re working on changing that.

What is Sentiment Analysis?

Currently, sentiment analysis is defined as the interpretation of text in social posts, to understand the feeling or attitude of the people who wrote them.

Sentiment analysis, (also known as opinion mining or sentiment tracking), is a feature of many social platforms on the market today. The feature usually consists of a simple thumbs up/thumbs down, or smile/frown to indicate positive or negative sentiment. It’s been around for quite a few years now, and it hasn’t really changed much!

Sentiment Analysis rarely tells you the whole story.

What’s good about it?

These days, the whole world is wearing their hearts on their news feeds. It’s no secret that the past decade has shown a global increase in public displays of opinion, and this shift has created a new way for businesses to learn about their current customers, acquire new ones, mitigate PR disasters and stay reputable. It’s a way for companies to turn our oversharing and humble-bragging into market intelligence, improving their reputation and their bottom line.


Where does it fall short?

Anybody who’s ever broken up with someone via text message knows the struggle. Text is often misunderstood, diluted, and misleading, especially when you’re working with 140 characters or less. (ENTER, the Emoji.) 

At best, text analysis as it stands today, offers a cool way to see the most rudimentary interpretation of the emotion contained within a post.
At worst, the analysis tool fails to perceive tone – such as sarcasm, and interprets the emotion in the complete opposite way from which it was intended.


For example:

“I *love* mornings.”

with a picture of a sunrise means something VERY different to,

“I *love* mornings”

with a picture of coffee grounds all over the kitchen floor.

(Example borrowed from Sentiment Analysis. Done Better – By Echosec CEO Karl Swannie) 



Sentiment analysis has a long way to go in its evolution.

How could it be better?

There’s more to the story than words alone…

Sentiment analysis will soon be a source of incredible value for your brand. The next iteration of opinion mining will factor in written emotions, such as surprise, disgust, anger, confusion, as well as meaningful context such as location, time, and current events. This will give organizations an incredible depth of knowledge about their customers.

With advancements in machine learning, and the Internet of Things (what’s this?), we are starting to get a much clearer and more accurate picture of the conversation. More context and a broader set of emotions give us the power to turn sentiment analysis from a simple +/- gauge to a whole new space to explore.

To sum it up,

The analysis of text alone only delivers a small piece of the emotional puzzle.

Be the first to get the complete picture of public opinion.

Understanding text, imagery, time, place, and overall context is the way forward for maintaining your brand health online.

Want to learn more about Sentiment Analysis?


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