Sentiment API Exposes Web’s Feelings

I recently wrote this article for Evri's blog. I'm reproducing it here in its entirety for the reading pleasure of all you ChaloBolo-ites.

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Every minute of every day people are expressing their sentiments and writing them down in news articles, blog posts, and other web content. Many people are too famous to write down their sentiments, but journalists, bloggers and other content creators are more than willing to document their feelings. Often times a famous radio commentator will bash a politician, or a politician will thrash a Hollywood actress. And on occassion, a true act of heroism will be recognized, and all sorts of famous folk will follow up with praise. Whether depressing or uplifting, disturbing or unnerving, tapping in to the sentiments of key actors on the world stage can be highly informative and engaging.

I'm excited to announce the release of our new sentiment web API which lets you build applications around the sentiments of specific entities (i.e. people, places, or things) as well as categories, or facets. Every minute of every day, Evri's systems are busy scouring the web, reading news content, blog posts and more so you don't have to. Now, Evri's system is also understanding the sentiments, or positive and negative expressions by and about entities. Many types of applications can be built using the sentiment API in areas including, but not limited to: market intelligence, market research, sports and entertainment, brand management, product reviews and more. Specifically, Evri's new sentiment API lets you:

• Find the percentage of positive and negative expressions of sentiment made by an entity, or about an entity. For example, find out what percentage of things being written about the iPhone are positive and which percent are negative.
• Discover who is criticizing and who is praising a particular person, place or thing. For example, see who is criticizing and praising Microsoft right now.
• Read what praisers and critics are saying about an entity. For example, see what the GOP are saying about the Democrats.
• Discover who or what your favorite entity is bashing and why. For example, see who Lance Armstrong is complaining about.
• Discover who or what your favorite entity is praising and why. For example, see who the World Health Organization is commending and why.

Now, as an exploratory exercise, or tutorial, on how to use the API, I will walk through the calls needed to make a widget called the Vibology Meter. So, imagine the widget below is externally configured to be about the entity Barack Obama corresponding to the Evri URI: /person/barack-obama-0x16f69. Upon first load, you see something that looks like this:

From the above screenshot, we can see that the percentage of positive sentiment and negative sentiment expressed by Barack Obama are displayed. We can also see the specific top entities being praised by Barack Obama in the left column, and the specific entities being criticized in the right column. For example, from the above screenshot, we see that Barack Obama is criticizing the GOP, Rush Limbaugh, the ACLU, Al Zawahiri, and Israel. In order to render the screenshot above, this sentiment summary information is returned by the following REST API resource call:

/v1/sentiment/summary?sentimentSource=/person/barack-obama-0x16f69&includeSummaryDetails=true&sort=date

Now, consider the use case outlined in the screenshot below, where the user clicks on [Anything] under the positive vibes sentiment. In order to get the results outlined to the right of the positive and negative sentiment columns, we execute a resource request like:

/v1/sentiment/about?sentimentSource=/person/barack-obama-0x16f69&sentimentType=positive&sort=date

From this request URI, we see that the sentimentSource references Barack Obama, meaning we are interested in vibes or sentiment expressed by Obama, as opposed to about him. Next we see the sentimentType is set to positive, meaning we are interested in positive sentiment expressions. Finally, we see sort=date meaning we are interested in the latest results.

Also from the screenshot below, we see the results of this resource request, namely, the specific snippet from the article, as well as a time stamp, the article title, and a link off to the source article. From the snippet, we see the sentence stating that "the president commended..." -- the Evri system recognizes "the president" to be the source of the vibes, or sentiment, and commendation to be the prime justification for his positive sentiment expression.

And finally, we consider the case illustrated below, where the "Receiving vibes" tab is selected, and the particular source of negative sentiment is chosen by the user to be Rush Limbaugh. In this case, by executing this resource request:

/v1/sentiment/about?entityURI=/person/barack-obama-0x16f69&sentimentType=negative&sentimentSource=/person/rush-limbaugh-0x1ebf5&sort=date

From this request URI, we see that the entityURI references Barack Obama, meaning the returned sentiment is about Barack Obama. We can also see that the sentimentType is set to negative, meaning returned sentiment expressions will be negative in nature. We also see that the sentimentSource references Rush Limbaugh. The URI referencing Limbaugh was obtained from the sentiment summary results of the request shown above in reference to the first screenshot.

That pretty much sums up our walk through with the sentiment API. For complete documentation on this new API resource, see the Get sentiment information section of our REST API Specification. And finally, if you have any comments on how we can make this API better, questions on how to get things to work, or examples of bugs, please let us know on our developer forum.