Veda Semantics Building intelligence through semantics

Text Analytic

s

Text Analytics

OntologyBuilding

Context Analysis

Sentiment Analysis

MachineLearning

Introduction

Natural Language Processing – use cases and Discovery product

Text analytics – use cases, Prism and Txt products

Examples of Veda projects and capabilities

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About Semantic technology

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Semantic technology is a language processing framework that helps make sense of unstructured data lying in documents such as PDFs, Word documents, Emails

Highlights of Semantic Technology

Process data in a manner similar to how the human mind understands data

Example: Joe works for XYZ Corp

A semantic framework through its linguistic processing models understands that Joe is a name, works is a verb and XYZ Corp is an organization.

Extract concepts and sentiments from any sentence

Example: Joe loves the seats of the Honda Civic

Semantic frameworks combined with lexicon engines auto classify the above sentence as a positive sentiment for the seat of a car.

Establish linkages between data across heterogeneous sources

Example: Joe works for XYZ Corp (Document 1) ; Joe loves the seats of the Honda Civic (Document 2).

Joe

XYZSeats

PDF Word Emails Social Media

Semantic Engine

Link Analysis Structured Datafor search

Sentiments

A semantic technology companyVeda Semantics has expertise in both Natural language processing and Statistical text mining techniques for Big Data scenarios

About Veda Semantics

Experienced teamKey members of technology team each have over a decade’s worth of experience each in Semantic technology

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Reputed leadership

Mr. V. Srinivasan – Chairman of the group with over 30 years of experience in Banking and IT, and ex-global MD & CEO of 3i Infotech Ltd.

Mr. Rajat Kumar - CEO who is a Wharton and McKinsey alum, with experience across diverse geographies and functions.

Focus Areas• Text Analytics through use of Statistical Algorithms with a

NLP overlay• Sentiment Analysis through NLP and Lexicon Engines

Veda Semantics has been recognized by Gartner in two separate reports (Who’s Who of Text Analytics, September 2012, and Report on BI platforms in Asia, January 2014)

About Veda Semantics

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Veda Difference

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Entity ExtractionRecognizes people, events, date, organizations automatically

Veda DifferenceUnlike traditional search which is based on keywords, Veda’s technology backbone is a combination of advanced statistical andlanguage processing algorithms then help not only understand data contextually but allow a user to FIND what they are looking for with a very high level of relevance and KNOW what they need to look for if they have no clue where to start

Veda’s sentiment engine deep dives to identify sentiments at clause level that translates into actionable insights at a productattribute level

Key Features

Document Classification Groups similar documents together for easier search

Concept Extraction &LinkagesAutomatically extracts key concepts from text, classifies them and associates related concepts across documents

HadoopAbility to process large volume data over commodity hardware in parallel

Sentiment Analysis at Attribute LevelExtracts sentiments and attaches them to attribute of a product

Data CorrelationGets correlation between related terms

Veda Difference

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Sentiment Analysis companies

Veda PrismText Analytics

Companies

Keyword Search

Bag of Words

Document Classification

Visual Entity Segregation

Related term and action association

Sentence Based Sentiments

Connecting to multiple sources

Response Dashboards

Clause Level Sentiment

Easy taxonomy creation

Competition monitoring at attribute level

Veda Discovery

Veda Technology Stack

Proprietary Linguistic Processing CapabilitiesVeda’s linguistic processing capabilities including Entity Extraction, Anaphora Resolution, Clause Level Identification are proprietary. The core technology has evolved with year’s of R&D thereby giving it a high level of accuracy

Ability to process unstructured data in multiple formatsConnectors to various sources including PDFs, Word docs, Excel, Outlook allow processing of data from heterogeneous sources and convert it into structured data stores

Patent for visual entity segregationVeda has filed a patent for visual entity extraction. Even without supporting context, the engine can pull out relevant information based on the document structure. Veda is also filing a patent for the proprietary clause based sentiment technology

User Interface allows sophisticated charting and drill down to get to the bottom of thingsUse of intuitive charts and other advanced front end charting technologies allow for data visualization at a whole new level

Robust Architecture and Seamless IntegrationVeda’s robust architecture of which Hadoop and Storm are a key component allow for real time and batch processing of millions of records. Our technology stack can be readily integrated with any application

Introduction

Natural Language Processing – use cases and Discovery product

Text analytics – use cases, Prism and Txt products

Examples of Veda projects and capabilities

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Sample use cases – NLP & Sentiments

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Reputation Management

Product Improvement

Areas

Monitoring large volume social media feeds and converting them into actionable insights

Provides connectors to social media to monitor feeds across Twitter, Facebook, blogs, internal emails and throws sentiments for each user, location, etc.

Tracking sentiment for products, competitors. Monitoring online reputation and responding to negative publicity

Veda’s platform allows real time monitoring of social feeds to understand swings in customer sentiment and allows companies to act on them immediately

Monitor customer feedback, internal feedback for products across email, chats, forums to understand customer feedback

Details product attribute level sentiment and action terms that makes feedback highly actionable

Medical Development

Monitoring online posts of patients to check for possible adverse psychological reaction to test drugs

Advanced sentiment engine can track trends over time, allowing a comparison to be made between pre and post drug use

NLP can be used to track employee suggestions, motivation levels and use as an input in product launch or project success predictions

Employee suggestions can be tracked deeply and in aggregate in a mater of a few clicks. Easy hierarchy building allows a top level view of what critical areas require immediate management attention

Sentiment Tracking

Employee Suggestions

Industry Challenge

Veda Difference

Benefits of using Veda in Voice of Customer

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Clause level sentiment identification gives sentiments at a attribute level that gets grouped at a category level

The Veda Semantics platforms havean ability to ingest various forms ofdata, including txt and worddocuments, PDFs and Excel

Response dashboard allows users to instantly respond to negative comments or sentiments

Customer Support for a large FMCGCurrently, it is difficult to get a high level summary into the areas of poor service. The Veda Semantics engine gets feedback data from multiple sources such as Email, Chat, etc. which is unstructured and structures it in intuitive categories.

Veda Edge

Sentiment Time Series allows users to look at and deep dive into sentiments for specific time periods, across locations

A list of top influencers allows a check into critical people who need to be addressed on social media

Veda Engine

Competition analysis allows for side-by-side comparison for competitor products

Features of Veda Discovery: Our flagship product for sentiments

Real Time Monitoring

Sentiment Scoring/Averaging

Competition Mapping

Time Series/ Influencer Analysis

Social Responses

Depth (Clause Level)

Veda provides real time monitoring of social media feeds for real time insights and responses

Allows trends over time to be considered with the ability to deep dive into a particular time period. Displays top influencers and sentiments around them

A critical benefit of using the Discovery product that traditional sentiment engines tools do not have is Clause level identification and mapping. Veda can look deep into a sentence to determine what a sentence is talking about

Sentiments are scored from a scale of -5 to +5. ‘Do not like’ is less negative than ‘hate’, and ‘amazing’ is more positive than ‘great’. Veda’s algorithms work to provide average scores across reviews for each aspect being considered. Comments indicating Intent to buy are highlighted separately.

Side by side monitoring display of competition. Can be done not only at the overall brand level, but also at the attribute level (e.g. perception about own vs. competitor price / quality / looks, etc.)

Respond to social messages through the dashboard itself

Veda Discovery – Sentiment analysis

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Domain Example sentence Sentiment – Other Engines

Sentiment - Veda

Electronics I love the screen of the phone. Positive Sentence Positive sentence, assigns it a score, and link the positiveness to ‘screen’

Hospitality I love the room, but hate the service!

Neutral Sentence Positive for ‘room’ Negative for ‘service’Assigns marks to each attribute

Fashion / cosmetics This perfume is like the other perfume!

Positive Sentence (seeing the word ‘like’ in the sentence)

Neutral Sentence

Airlines I cannot say the attendant was friendly.

Positive Sentence Negative Sentence (recognizes negation)

BFSI I prefer Fund A over Fund B. Positive Sentence Positive only for Fund ANegative for Fund B

Automotive Despite the good steering, it has an underpowered engine.

Neutral Sentence Positive for ‘steering’ Negative for ‘engine’Assigns marks to each attribute

FMCG Has great cleaning power and does not irritate hands.

Neutral sentence (‘great +

irritate’)

Positive for ‘cleaning power’ Positive for ‘hands’Assigns marks to each attribute

Veda Discovery

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Step 1Connect Real time Social Media feeds or Upload excel data or connect to outlook to extract emails

Step 2Process the data through the Discovery Engine

Step 3Get sentiments at an attribute level

View Time Series Analytics and Top Influencers

Go to messages and respond to critical ones, or check messages by location

Veda Discovery – Delivery process

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Understand client needs

• Objective of usage

• Type of data in use

• Need for pre-post processing

Align and integrate Discovery features

• Preprocessing

• Domain data machine learning if needed

• Specific ontologies

• Integration options

Provide regular reports in addition to dashboards

• Customized reports

• Dashboard formats

• Output to integrate with other systems

Can be offered either on a SaaS model or as a full service model

Introduction

Natural Language Processing – use cases and Discovery product

Text analytics – use cases, Prism and Txt products

Examples of Veda projects and capabilities

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Sample use cases – Text Analytics & NLP

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Fraud detectionEnvironment Monitoring –

Sifting through thousands of documents that may need to be presented as court evidence

Veda’s text mining platform allows for easy identification of which document is relevant without having to read through all documents

This helps not only in cost reduction, but also in timely compliance

In insurance and warranty claims, specific patterns that may go unnoticed over thousands of claims can be identified and presented for further analysis.

Through Veda’s platform it becomes easy to identify cases where illness and medication appear unrelated

Continuously monitor new regulations, competitive moves and key customers for reference

Veda’s Discovery platform allows for social monitoring and this can be used for predictive analytics in sales data

Investigations and Forensics

Detecting fraud or getting to the bottom of it involves large volume data without knowing where to start

Veda’s capability in Entity and Concept extraction, deriving insights from seemingly unconnected pieces of information can be extremely useful in guiding investigations in the most promising direction, creating integrated data repositories, as well as in early threat identification and response

Monitoring feedback from customers across multiple channels and deriving actionable insights

Veda’s edge is the ability to not only extract sentiments but do this at a product attribute level. Customer care executives can be provided this information in near real time

Legal DiscoveryCustomer Feedback

Industry Challenge

Veda Difference

Benefits of using Veda in Investigations

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Named Entity Extraction Engine throws up Names, Dates, Organizations and Linkages

The Veda Semantics platforms have an ability to ingest various forms of data, including txt and word documents, PDFs and Excel

Document Similarity Engine throws up similar documents and clusters similar documents for easy viewing

Corporate Fraud ScenarioProcurement department colludes with vendors to get quotes that are very close to budget. The communication involves repetitive email patterns and certain words that are highly correlated.

Veda Edge

High Frequency Terms across Documents thrown up gives a starting point for deep search

Associated terms and actions allow users to get to a relevant message in 3 clicks

Veda Engine

Features of Veda Prism: Our flagship product for text mining

Automated Term Extraction

Connected Terms

Document Similarity

Natural Language Processing

Terms can be automatically extracted from a set of documents and can be analyzed in multiple ways, e.g.:

What are the high frequency terms in a document and across documents

What are terms that appear often, but only in a limited set of documents

While doing this analysis, synonyms, e.g. phone, telephone and cellphone can be automatically clubbed and displayed

Terms that are connected to a chosen term are automatically displayed. This allows for immediately focusing attention on terms or term pairs that are more relevant than others

Documents that are similar to each other / target document can be automatically extracted. There is no need to look for specific keywords to look for relevant documents from a large corpus

A critical benefit of using the Prism product is the overlay of Natural Language Capability

• Allows for extraction of phrases, not just single words

• The ability to connect verbs to topics provides allows making of categories based on term and action combinations (e.g. ‘please renew subscription’ and ‘please do not renew subscription’ can be categorized separately)

• When coupled with Veda Discovery engine, allows for sentiment to be extracted from documents, in case required, to identify tonality of documents and focus efforts on highly negative documents in certain cases

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Sample dashboards of Veda Prism

Step 1Connect your data sources. Upload excel data or connect to outlook to extract emails

Step 2Process the data through the Prism Engine

Step 3

Get key concepts, term frequencies, and document frequencies

View associated actions and words that are of importance to you

Go to the message

Sample dashboard of Veda Txt (Named Entity Extraction)

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SummaryVeda Txt is the proprietary EntityExtraction Engine that throws out multiple features of text from any news article. The features include People, Places, Events, Festivals, Organizations, Money, Quantity, Date, Facilities, Designation, Sports etc. The engine is trained on millions of news and other corpus

UsageCan plug into both the Prism and Discovery platforms to allow for tagging and linking of Named Entities mentioned above

Introduction

Natural Language Processing – use cases and Discovery product

Text analytics – use cases, Prism and Txt products

Examples of Veda projects

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Examples of Veda projects

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Client Profile Project Description

A leading recruitment firm Automatic resume matching with job descriptions based on sector ontologies, allowing for faster and more accurate matching of candidates with profiles

A global publishing house in legal, tax, finance and healthcare

As part of a tax workflow, the Veda capability allowed a user to look for related content and caselaws automatically depending on data being entered

The capabilities applied included ontology modeling and workflow creation

A prominent product manufacturer on inference and reasoning engine

Leveraged semantics for a supply chain process to integrate systems with heterogeneous data sources and help in automatic decision making in case of any disruptions in the cycle.

Provided ontology modeling and application development services

A reputed university and complex systems research lab in Australia

Used ontology modeling to produce a method for organizing and potentially navigating the wide range of web-pages associated with the Murray-Darling river system in a seamless fashion

An analytics software manufacturer in Australia

Used named entity recognition, linkages and ontologies to assist investigation of fraud and terrorism and in establishment of links between entities

A premier worldwide online providers of news, information and shopping services

Developed a web analytics platform for analyzing click-stream data in real-time

Veda Deployment Models

On-the-CloudBoth the Veda Prism and Discovery will be available on the cloud where the end customer accesses them through a web interface for text and sentiment analytics. The application will have connectors to Social Media and Outlook Upload to allow users to do social media monitoring

Enterprise DeploymentVeda also caters to large organizations that need a bespoke deployment of Semantic frameworks and capabilities offered by Prism or Discovery through a combination of license and implementation model

Hub and SpokeOur API’s are available for use by anyone who wants to build proprietary analytics or wishes to integrate with an existing Business Intelligence platform

Contact details

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Veda Semantics Pvt. Ltd.

www.vedasemantics.com

Contact person:Rajat Kumar (CEO)rajat@vedasemantics.com# +91-9619308745

3rd floor, Sai Arcade, No. 56, Outer Ring Road,Devarabeesanahalli, BellandurBangalore, Karnataka, India

605 One Lake Plaza, Cluster T, Jumeirah Lake Towers, Dubai, UAEP.O. Box: 32620Phone: +971 5 2929 6000