IBM Watson Tradeoff AnalyticsAlChemy

Helping people make the right decisions

Mohammad Ahmed , Executive IT Specialist.IBM WATSON, APM, Hybrid Cloud.

DHAKAIYA@US.IBM.COM

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What is IBM Watson Tradeoff Analytics?

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Tradeoff Analytics help people weigh their options and make the right choices.

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Narrow down to the best options to meet

multiple goalsExplore visually Recommend based on

the preferred choices

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Tradeoff Analytics is a Watson Cognitive Service on IBM Watson Developer Cloud

Access the service on Watson developer Cloud at: http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/tradeoff-analytics.htmlAccess the Public Wiki Public WIKI: https://w3-connections.ibm.com/wikis/home?lang=en-us#!/wiki/People%20Insights%20Services%20on%20WDC/page/Tradeoff%20Analytics

Three Pillars of the Tradeoff Analytics Technology

Applying mathematical models to narrow down the number of choices to only the superior alternatives

Applied Mathematics

Facilitates results interpretation in a consumable and interactive mannerVisual Analytics

Provides breakthrough tools that can help guide the decision maker through the decision process, recommends preferred options, and analyzes and highlights decision tradeoffs.

Cognition

What does Tradeoff Analytics do?

The IBM Watson™ Tradeoff Analytics service helps people make better choices by:1) Using mathematical filtering techniques to identify the top options based on multiple

criteria2) Helping decision makers explore the tradeoffs between options when making

complex decisions. 3) Combining smart visualization and analytical recommendations for easy and intuitive

exploration of tradeoffs. 4) Filtering out less attractive options based on a user’s specified objectives,

preferences, and priorities5) Encourage the user's exploration of the remaining optimal candidates6) Helps decision makers consider only the goals that matter most and only the best

options to make a final, informed decision.

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Tradeoff Analytics helps people make better choices while taking into account multiple, often conflicting, goals that matter when making that choice.

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Investment Scenario

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© 2013 IBM Corporation

Meet Emma! Emma is a professional living in New York City, working at a large Investment Bank. She is looking to compare mutual funds (115 of them), across the following performance time dimensions, as well as their Risk:

• Maximize Year To Date Performance• Maximize Short Term (1 year) Performance• Maximize Mid Term (3 years) Performance • Maximize Long Term (10 years) Performance• Minimize Risk

Picture taken from MAM asset: http://mam2.stibo.com/PORTAL/SEARCHRESULTS.php?page=1&id=483749&path=/data/IBM_MCW/IBM_Images/IBM%20Product%20or%20Royalty%20Free%20Images

Demo TradeOff Analytics

IBM Watson AlchemyLanguageAPI functions which use sophisticated natural language processing techniques to analyze your content

© 2017 International Business Machines Corporation

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AlchemyLanguage is a collection of 12 APIs that offer text analysis through natural language processing. The AlchemyLanguage

APIs can process text and help you to understand its sentiment, keywords, high-level concepts and more.

APIs enable you to:

•Quickly extract meaningful information from unstructured data

•Categorize and label text and images

•Analyze sentiment

•Connect your data to additional knowledge sources

•And more…

Most Popular APIs

•Keywords

•Named Entity Extraction

•Relations Extraction

•Sentiment Analysis

•Taxonomy

•Concept Tagging

An IBM Company

80% of the world’s data is unstructured

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Problem:

•Inability to mine unstructured data sets: 90% of the world’s data was created in the

past two years, 80% of which is unstructured

•Limited supply skills in NLP / AI to “do it yourself”

•Traditional approaches are brittle and expensive to maintain

Resulting in:

Lost revenue, missed opportunities, increased operational costs (manual vs automated

approaches), inability to detect / prevent customer churn, etc

© 2017 International Business Machines Corporation

Natural Language Processing with AlchemyLanguage

Natural Language Processing gives structure to unstructured text by extracting important metadata, such as keywords, entities, and sentiment.

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© 2017 International Business Machines Corporation

How does it work?

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AlchemyLanguage offers multiple API functions as part of its text analysis service, each of which uses sophisticated natural language processing techniques to analyze your content.

• Entity Extraction• Sentiment Analysis• Keyword Extraction• Concept Tagging• Relation Extraction• Taxonomy Classification• Author Extraction• Language Detection• Text Extraction• Microformats Parsing• Feed Detection• Linked Data Support

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Key Capabilities

Concept Tagging: high-level concepts in text (e.g. this article is about monetary policy)

Taxonomy Classifier: hierarchical categorization (finance/personal finance/credit card)

Entity Extraction: what are the entities (people, places, organizations, etc.) in text

Sentiment Analysis: how are people talking about the entities (positive, negative)

Emotion Analysis: what emotions are people expressing (joy, fear, sadness, anger, disgust)

Linked Data Support: appending additional information about the entity

Microformats Parsing: semantic information embedded in the article

Text Extraction: extract the important parts of text within an article

Relation Extraction: subject / action relations between entities

Language Detection: what language is this written in

Feed Detection: find the RSS feeds in the website

Keyword Extraction: important topics in content

Author Extraction: who wrote the article

AlchemyLanguage supports the following features:

Demo AlChemy & Visual

Use Cases

• Social Media Monitoring• Business Intelligence• Content Discovery• Research

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ibmwatson.com

facebook.com/ibmwatson

@ibmwatson

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Key Capabilities

Backup