Tomato or Tomahto? How Next Caller uses kimono to add pronunciation to caller ID

Next Caller provides Advanced Caller ID™ to businesses. Across industries, inefficient exchanges between sales/support reps and customers cost $14B annually. With the Next Caller API, unknown inbound phone numbers get matched with relevant background data for the caller, giving reps near-instant access to all relevant data on a customer.

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Hacking real estate to find the best off-market deals

Winning in real estate is about better information, with data sources like Redfin and MLS as table stakes. The most successful real estate players win by having an information edge. Kimono is a smart web-scraper – letting innovative real estate players like Michael Tomko find leads that others can’t. Here’s how Michael identifies high value properties and unlocks the best deals on the market.

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Fox vs. CNN: Who’s got Obama on the mind?

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Fox News on 1/29/15

CNN on 1/29/15

Turning the mountains of unstructured text scattered across the web into insights can be a daunting prospect. At kimono, we are working to make this much easier. Take news for example – New York Times alone publishes 350+ pieces of content per day; The Huffington Post releases 1,200. With just a few kimono APIs, we can create a structured corpus of text that we can mine to understand trends, biases and patterns across sources. We’ll make our first scratch on the surface here by setting up APIs for CNN and Fox News to ‘read’ every article on each site’s respective front page and compare what meaningful words are being said.

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Visualize a year of bike rides with Kimono and CartoDB

Tutorial: Map your own location data

Data is more accessible, tangible and interesting when you can visualize and interact with the figures on a page. That’s why we love teaming up with our friends over at CartoDB! Kimono is a smart web scraper that let’s you turn data on a website into an API – a structured feed of updating data. CartoDB let’s you take that data set and create beautiful interactive maps. In this post, we will use kimono to get over a year’s worth of bike trip data from New York City’s citibike bike sharing program. We’ll then use cartoDB to plot our friend Andrew’s movements on a map. A big thanks to Andrew for riding his bike a lot and sharing his data with us!

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Guest blog: Sentiment analysis on web scraped data with kimono and MonkeyLearn

Contributed by Raúl Garreta, co-founder of MonkeyLearn

New tools have enabled businesses of all sizes to understand how their customers are reacting to them – do customers like the location, hate the menu, would they come back? This increased volume of data is incredibly valuable but larger than any mere mortal can assess, understand and turn into action. Several technologies have emerged to help businesses unlock the meaning behind this data.

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Guest Post: The Semantic Web is dead – long live the Semantic Web!

Contributed by Dirk Stähler, author of “Tracking Information on Today’s Internet”.

The Semantic Web, the World Wide Web Consortium’s 2001 proposal to standardize the web to a consistent and structured format, has failed to gain traction in any significant way more than ten years later. However, by empowering individuals with new tools to structure, process and create from niche web content, we can achieve the vision of the Semantic Web with a different approach.

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Guest Post: Looking beyond the star rating – sentiment analysis for restaurant reviews

Contributed by Dillon Robinson. See his website here.

The timing was perfect. I’d just had bad food at a supposedly delicious restaurant, when my brother began to rant about this neat new thing he had recently discovered called “kimono”. He had used kimono to build a web app to grab the restaurant’s UrbanSpoon rating and make fun of the (mediocre) score. It was a small and amusing test of what kimono could do. At the moment I was browsing the restaurant’s Yelp page, and that got me thinking — could I use kimono to pull data from Yelp in a similar way?

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