Send us a Tweet, Facebook post or Image and we'll send you back a JSON annotation that describes the brands we can see.
You can HTTP POST the text in this tweet to our API:
"Snubbed by Apple, foursquare makes its maps experience better on iPhone http://tinyurl.com/d37w2ue by @JBruin #mapping"
and we'll recognise the brands, users, URLs and hashtags:
"Snubbed by Apple, foursquare makes its maps experience better on iPhone http://tinyurl.com/d37w2ue by @JBruin #mapping"
and we'll send back the following:
{"Brands":
{"Apple": {"span": [11, 15], "raw": "Apple"},
"FourSquare": {"span": [18, 27], "raw": "foursquare"},
"Apple": {"span": [65, 71], "raw": "iPhone"}
},
"URLs":
{"URL": {"span": [72, 98], "raw": "http://tinyurl.com/d37w2ue"}
},
"Users":
{"jbruin": {"span": [102, 109], "raw": "@JBruin"}
},
"Hashtags":
{"mapping": {"span": [110, 118], "raw": "#mapping"}
},
"BrandLinks":
{"Apple": {"URL": "http://apple.com", "Wikipedia": "http://en.wikipedia.org/wiki/Apple_Inc."},
"FourSquare": {"URL": "http://foursquare.com", "Wikipedia": "http://en.wikipedia.org/wiki/Foursquare"}
}
}
The clever part is our use of Natural Language Processing and Machine Learning to provide an Entity Recognition engine that correctly recognises brand terms from non-brand usages. Examples include: