Introduction - Grey Matter...configure the language model • Made it easier to interpret the users...

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Introduction Microsoft Cognitive Services Text Analytics | Sentiment Analysis | Language Understanding

Transcript of Introduction - Grey Matter...configure the language model • Made it easier to interpret the users...

Page 1: Introduction - Grey Matter...configure the language model • Made it easier to interpret the users goals (Intent) • Extract structured data (Entities) from a user sentence (Utterance)

IntroductionMicrosoft Cognitive Services

Text Analytics | Sentiment Analysis | Language Understanding

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Cognitive Services

https://azure.microsoft.com/en-gb/services/cognitive-services/directory/know/

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Twitter Innovation Challenge 2016

• Twitter’s annual event designed to highlight excellence in marketing technology.

• Highlights some of the most exciting technology in the industry and the chance to showcase their performance in a $50,000 Twitter ad campaign.

• Grand Prize awarded $100,000 in media credit, co-marketing opportunities, and recognition within the Twitter Official Partner Program.

Page 4: Introduction - Grey Matter...configure the language model • Made it easier to interpret the users goals (Intent) • Extract structured data (Entities) from a user sentence (Utterance)
Page 5: Introduction - Grey Matter...configure the language model • Made it easier to interpret the users goals (Intent) • Extract structured data (Entities) from a user sentence (Utterance)
Page 6: Introduction - Grey Matter...configure the language model • Made it easier to interpret the users goals (Intent) • Extract structured data (Entities) from a user sentence (Utterance)
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Text Analytics API Demo

-Creating API in Azure-Postman-C#

“I had a wonderful trip to Seattle and enjoyed seeing the

Space Needle!”

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Language Understanding (LUIS)

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Some of the challenges when processing human

language

• Not always clear what the underlying intent is

• Can be difficult to extract structured data in a sentence (Utterance)

• Can be difficult to code for all combinations

• I was looking to identify commercial intent insocial data

• Part of Speech (POS) Tagging can help – to apoint!

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LUIS to the rescue!

• Online dashboard to configure the language model

• Made it easier to interpret the users goals (Intent)

• Extract structured data (Entities) from a user sentence (Utterance)

• Easy to integrate with applications using a language of your choice

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LUIS

A machine learning-based service to build natural language into apps, bots, and IoT devices. Quickly create enterprise-ready, custom models that continuously improve.

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Main Components

UtteranceWhat the user has said, e.g. “I would like a new phone”

IntentWhat the user wants to do, “Order New Product” (or in this example, we’re using LUIS to help identify commercial intent “Sales.Lead”)

EntityAn item of interest in the Utterance

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LUIS Demo:- Web Dashboard- Postman- C#

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Results using LUIS

• Reduced development effort

• Scalable API that identifies users that are thinking of buying a product or service.

• LUIS helps determine a probability between 0 and 100 to determine how strong the commercial intent signal is.

• A solution that can segment and hyper-target users on social media and provide actionable insights for brands, marketers and businesses.

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Thankyou

Contact: www.jamiemaguire.net | @jamie_maguire1 | [email protected]

Tutorials: https://codematters.online/author/jamiem/Code: https://github.com/jamiemaguiredotnet