Hey everyone, thanks for tuning in for the last five weeks as I detailed my experiences integrating different sentiment analysis APIs. To close off, I wanted to quickly summarize my overall impressions and final thoughts on each API. Keep Reading →
Hey everyone, this week’s integration showcase is Bitext and this will be the last of the five sentiment analysis APIs that I’ll be writing about. The previous ones I’ve covered were Viralheat, Chatterbox, Semantria, and AlchemyAPI. For one last time, I will try to be as objective as possible and highlight all aspects of the integration from my personal experience.
Bitext is another text analytics solution that offers a sentiment analysis API. The Bitext API doesn’t fall under the conventional definition of an ‘API’, because the exposed end-points don’t follow REST or any other API principles. Instead they utilize web forms built on an ASP.NET platform. Technically, to access their services, a user just needs to make a POST (submit) request to a remote form, which in turn responds with analysis results.
Hey everyone, thanks for continuing to read my coverage on integration. This week is Viralheat’s turn in the spotlight. In case you missed it, I covered Chatterbox, Semantria, and AlchemyAPI in my last posts. Go check them out if you’re interested. Once again, I will try to be as objective as possible and highlight all aspects of the integration from my personal experience.
Viralheat’s main business is in social media marketing, however along with their main product, Viralheat offers several APIs for 3rd party integrators. For this project I used their sentiment API (https://app.viralheat.com/developer/sentiment).
Hey everyone, thanks for checking in again. This week, I’ll be covering my integration experience with Chatterbox. If you missed it, I covered Semantria last week, and AlchemyAPI the week before that. Again, I will try to be as objective as possible and highlight all aspects of the integration from my personal experience.
The API is simple and supports only a few basic features, sentiment analysis being one of them. Chatterbox doesn’t have an SDK available, so I was forced to implement everything manually. This wasn’t a major problem, because of the simplicity of the implementation. Authentication was done through the custom header and a simple API key, so the entire code related to sentiment analysis requests looks like the following:
Hey everyone, last week I went over my experience with AlchemyAPI integration. This week, I’ll be covering my experience with Semantria. Once again, I will try to be as objective as possible and highlight all aspects of the integration from my personal experience.
Semantria is a relatively young SaaS solution that utilizes Lexalytics’ Salience core engine under the hood, one of the best NLP engines on the market. As the CTO of Semantria, I am obviously biased in thinking that we are the Superman of all sentiment APIs, but I will try to remain as objective as possible and even expose some of our kryptonite.
Hi everyone, this is my first technical post on SemantAPI and I hope it won’t be the last. In the coming weeks I’ll be detailing my first-hand experience integrating the sentiment analysis APIs used with SemantAPI. I will try to be as objective as possible and highlight all aspects of the integration from my personal experience.
It wasn’t easy to decide which services should be used in the SemantAPI toolkit. In the end, I think we chose the 6 best sentiment APIs on the market, all of which claim the highest accuracy of sentiment analysis out of the box. The APIs featured in SemantAPI include:
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 →
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 →
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.
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 →