Good Contents Are Everywhere, But Here, We Deliver The Best of The Best.Please Hold on!
Your address will show here +12 34 56 78

Our resident scientist, Peiman Barnaghi has just returned from a trip down under for KDD ‘2015 in Sydney, Australia. The annual conference, on Knowledge Discovery and Data Mining (KDD), is the premier international forum for Data Science, Data Mining, Knowledge Discovery and Big Data. The conference brings together practitioners, researchers, academia and industry professionals to share knowledge, ideas and findings in a highly educational environment.

 

kdd2015

 

The conference supported by the likes of Google, Yahoo, Alibaba, Baidu and Facebook featured 4 Keynote presentations, 11 talks, a whopping 228 paper presentations, tutorials and workshops as well as poster sessions by researchers, all spread across 4 floors of the Hilton in Sydney’s CBD.

Given the fantastic opportunity for learning and exposure, Peiman was delighted to be invited along to KDD ‘15 to present a recent paper he completed “Text Analysis and Sentiment Polarity on FIFA World Cup 2014 Tweets” as part of a workshop, entitled “Large-Scale Sports Analytics”.

 

kdd2015

 

Peiman’s paper aims to examine the effectiveness of a machine learning method for providing positive or negative sentiment on tweets and to find correlations between such sentiment and real-life events such as a team scoring in a game of football, which is a part of Peiman’s ongoing research in Sentiment Analysis on streaming feeds. You can download Peiman’s publication here.

Peiman’s top picks from the various talks, tutorials and research sessions are listed below:

Keynote Presentation:

Online Controlled Experiments: Lessons from Running A/B/n Tests for 12 years by Ronni Kohavi, General Manager, Analysis and Experimentation, Microsoft (Download Slides)

Tutorials:

Web Personalisation and Recommender Systems, delivered by Shlomo Berkovsky and Jill Freyne from CSIRO. (Download Slides)

Automatic Entity Recognition and Typing from Massive Text Corpora – A Phrase and Network Mining Approach. (More Info and Slides)

Research Sessions:

Each research sessions dealt with ~5 topics delivered by various speakers, which are all listed at the links below.

  • Topic Models and Tensors (Link)
  • Big Data (Link)
  • Knowledge Discovery (Link)
  • Sampling and Streams (Link)





Text Analysis API - Sign up




0

Most of our users will make 3 or more calls to our API for every piece of text or URL they analyze. For example if you’re a publisher who wants to extract insight from a an article or URL it’s likely you’ll want to use more than one of our features to get a proper understanding of that particular article or URL.

With this in mind, we decided to make it faster, easier and more efficient for our users to run multiple analysis operations in one single call to the API.

Our Combined Calls endpoint, allows you to run more than one type of analysis on a piece of text or URL without having to call each endpoint separately.

  • Run multiple operations at once
  • Speed up your analysis process
  • Write cleaner, more efficient code

Combined Calls

To showcase how useful the Combined Calls endpoint can be, we’ve ran a typical process that a lot of our news and media focused users would use when analyzing URL’s or articles on news sites.

In this case, we’re going to Classify the article in question and extract any Entities and Concepts present in the text. To run a process like this would typically involve passing the same URL to the API 3 times, once for each analysis operation and following that, retrieving 3 separate results relevant to each operation. However, with Combined Calls, we’re only making 1 call to the API and retrieving 1 set of results, making it a lot more efficient and cleaner for the end user.

Code Snippet:

var AYLIENTextAPI = require('aylien_textapi');
var textapi = new AYLIENTextAPI({
    application_id: "APP_ID",
    application_key: "APP_KEY"
});

textapi.combined({
    "url": "http://www.bbc.com/news/technology-33764155",
    "endpoint": ["entities", "concepts", "classify"]
}, function(err, result) {
  if (err === null) {
    console.log(JSON.stringify(result));
  } else {
    console.log(err)
  }
});

The code snippet above was written using our Node.js SDK. SDKs are available for a variety of languages on our SDKs page.

Results

We’ve broken down the results below into three sections, Entities, Concepts and Classification to help with readability, but using the combined calls endpoint all of these results would be returned together.

Entities:

{
    "results": [
        {
            "endpoint": "entities",
            "result": {
                "entities": {
                    "keyword": [
                        "internet servers",
                        "flaw in the internet",
                        "internet users",
                        "server software",
                        "exploits of the flaw",
                        "internet",
                        "System (DNS) software",
                        "servers",
                        "flaw",
                        "expert",
                        "vulnerability",
                        "systems",
                        "software",
                        "exploits",
                        "users",
                        "websites",
                        "addresses",
                        "offline",
                        "URLs",
                        "services"
                    ],
                    "organization": [
                        "DNS",
                        "BBC"
                    ],
                    "person": [
                        "Daniel Cid",
                        "Brian Honan"
                    ]
                },
                "language": "en"
            }
        },

Concepts:

{
            "endpoint": "concepts",
            "result": {
                "concepts": {
                    "http://dbpedia.org/resource/Apache": {
                        "support": 3082,
                        "surfaceForms": [
                            {
                                "offset": 1261,
                                "score": 0.9726336488480631,
                                "string": "Apache"
                            }
                        ],
                        "types": [
                            "http://dbpedia.org/ontology/EthnicGroup"
                        ]
                    },
                    "http://dbpedia.org/resource/BBC": {
                        "support": 61289,
                        "surfaceForms": [
                            {
                                "offset": 1108,
                                "score": 0.9997923194235071,
                                "string": "BBC"
                            }
                        ],
                        "types": [
                            "http://dbpedia.org/ontology/Agent",
                            "http://schema.org/Organization",
                            "http://dbpedia.org/ontology/Organisation",
                            "http://dbpedia.org/ontology/Company"
                        ]
                    },
                    "http://dbpedia.org/resource/Denial-of-service_attack": {
                        "support": 503,
                        "surfaceForms": [
                            {
                                "offset": 264,
                                "score": 0.9999442627824017,
                                "string": "denial-of-service attacks"
                            }
                        ],
                        "types": [
                            ""
                        ]
                    },
                    "http://dbpedia.org/resource/Domain_Name_System": {
                        "support": 1279,
                        "surfaceForms": [
                            {
                                "offset": 442,
                                "score": 1,
                                "string": "Domain Name System"
                            },
                            {
                                "offset": 462,
                                "score": 0.9984593397878601,
                                "string": "DNS"
                            }
                        ],
                        "types": [
                            ""
                        ]
                    },
                    "http://dbpedia.org/resource/Hacker_(computer_security)": {
                        "support": 1436,
                        "surfaceForms": [
                            {
                                "offset": 0,
                                "score": 0.7808308562314218,
                                "string": "Hackers"
                            },
                            {
                                "offset": 246,
                                "score": 0.9326746054676964,
                                "string": "hackers"
                            }
                        ],
                        "types": [
                            ""
                        ]
                    },
                    "http://dbpedia.org/resource/Indian_School_Certificate": {
                        "support": 161,
                        "surfaceForms": [
                            {
                                "offset": 794,
                                "score": 0.7811847159512098,
                                "string": "ISC"
                            }
                        ],
                        "types": [
                            ""
                        ]
                    },
                    "http://dbpedia.org/resource/Internet_Systems_Consortium": {
                        "support": 35,
                        "surfaceForms": [
                            {
                                "offset": 765,
                                "score": 1,
                                "string": "Internet Systems Consortium"
                            }
                        ],
                        "types": [
                            "http://dbpedia.org/ontology/Agent",
                            "http://schema.org/Organization",
                            "http://dbpedia.org/ontology/Organisation",
                            "http://dbpedia.org/ontology/Non-ProfitOrganisation"
                        ]
                    },
                    "http://dbpedia.org/resource/OpenSSL": {
                        "support": 105,
                        "surfaceForms": [
                            {
                                "offset": 1269,
                                "score": 1,
                                "string": "OpenSSL"
                            }
                        ],
                        "types": [
                            "http://schema.org/CreativeWork",
                            "http://dbpedia.org/ontology/Work",
                            "http://dbpedia.org/ontology/Software"
                        ]
                    }
                },
                "language": "en"
            }
        },

Classification:

{
"endpoint": "classify",
            "result": {
                "categories": [
                    {
                        "code": "04003005",
                        "confidence": 1,
                        "label": "computing and information technology - software"
                    }
                ],
                "language": "en"
      }
  }

You can find more information on using Combined Calls in our Text Analysis Documentation.

We should also point out that the existing rate limits will also apply when using Combined Calls. You can read more about our rate limits here.





Text Analysis API - Sign up




0