Archive for 2013

Chris West

If you’re a newspaper, you want to know what your readers think of you. But if you ask them online and millions reply, how can you be sure you’re listening to the right thing?

If you’re an online businesses, your competitors are only a click away, so it’s vital to know how your customers feel about you. And if you’re a newspaper with a sizable online readership, then you’d think that life is made easier by an abundance of readers’ forthright comments. But save those woo-hoos, dear Editor.

We’ve noticed 3 distinct problems. Text analytics can solve some of them, but there’s still a big issue over sentiment analysis (a phrase that we hear a lot, to be honest). Keep Reading →

Xinhua-News-Agency-Times-Square-LED-Billboard

When it comes to sentiment, we tend to think of news content as bearing the least. The whole mantra of most news sources is to report events as accurately as possible, and that usually means reporting the facts and refraining from any judgment calls. We all know this doesn’t actually happen and in fact some sources might even gloat in their particular bias.

When we as readers are aware of this bias, we can think critically about the information given to us, compare across sources, and make our own value judgments, but this takes time and effort. Furthermore, the way bias is conveyed isn’t always transparent. Keep Reading →

Photo Courtesy of Greg Younger

We all know sentiment analysis is useful for customer feedback. We know it’s important for social media monitoring, and customer experience management. We know it can be used for market research, and survey coding, and all the usual big business use cases that deal with lots of text and need it analyzed fast.

What I’m curious about, and what’s surprised me most in the years I’ve been working with this technology, are the unusual applications most people don’t even think about.

Here are three of the coolest use cases I’ve come across in the last year.

Keep Reading →

Disney

It’s always interesting to explain sentiment from a text or speech analytics standpoint to someone who has never heard of it before. Sometimes “emotion detection” is used, but everything has emotion, right? Right!? RIGHT! There are subtle differences in how we write and how we speak that make sentiment analysis particularly tricky.

First off, I’ll admit, it’s not very accurate from a numbers perspective. Today the technology just isn’t there to have 100% accuracy. But what does that even mean, 100% accuracy? Are humans 100% accurate? Think of the last time you got the wrong order at the drive thru. Sentiment is a subjective exercise at best and depending on cultural and geographic differences it can be a total guess at worst. Keep Reading →

semantapi sentiment analysis comparison tool

Welcome to SemantAPI!

I wanted to christen this community resource by explaining what this website is about. You’re already here, so it’s fair to assume that you know a little bit about sentiment analysis. This site is a community resource for anyone looking to learn about sentiment analysis.

I’d like to note that everyone is welcome on SemantAPI. Vendors, users, analysts, specialists, whoever and whatever you are, feel free to participate! Want to create a blog post? Go write ahead :P, and email me at semantapi [at] gmail with an interesting topic.

Keep Reading →