presentation by meshlabs at zensar #techshowcase - an ispirt productnation initiative
Post on 17-Oct-2014
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Presentation by Meshlabs at Zensar #TechShowcase - An iSPIRT ProductNation initiative.. Bangalore based firm; has a text analytics platform. Listens to all stake holders and unlocks the hidden value via text analytics.TRANSCRIPT
MeshLabs Text Analytics
© 2013 MeshLabs So0ware Private Limited
Confiden<al
About US
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Featured Customers:
Provider of text analy<cs so0ware products
Informa<on Management | Customer Experience Management | Business Intelligence | Regulatory Compliance
ü On-‐premise ü SaaS ü API
ü Unified Content Access ü En<ty Extrac<on / Tagging ü Categoriza<on ü Summariza<on ü Recommenda<on ü Faceted Search ü Sen<ment Analysis ü Dashboard & Repor<ng
Text, Text, Everywhere…
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Too much volume and variety Missed Opportuni1es
Product Managers Customer Insight Managers
Research Analysts Customer Care Reps
Sales & Marketing Leaders HR Leaders
Senior Executives
Cost / Quality concerns over manual methods Current BI tools won’t work
Structured data only and too complicated
And Not a Single Insight.
Multiple Channels, Sources and Types
Limited Analysis, Ad hoc, Scalability
Issues
Topline and Bottom-line Impact
Text Analytics
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Linguis<cs
Sta<s<cs
Seman<cs powerful technology to automa<cally…
Ingest all text data/content
Extract valuable assets
Deliver ac<onable insights
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How it Works
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1 v Connectors to Enterprise Content Stores, Facebook, Twitter etc.
v Crawlers for getting data from websites v Upload files & documents – Excel, Word, PDF etc.
2 Process your data – Extract entities, classify, cluster, and score sentiment
v NLP – Natural Language Processing v Taxonomies & Custom Ontologies v Machine Learning
3 Analyze output - dashboards, reports, workflows, and alerts
v Dashboards v Charts & Reports v Exports
Gather your data – Text (Unstructured) and Structured
Key Use Cases
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Informa<on Extrac<on
“How do I extract key informa<on from CRM Notes to
predict cross-‐sell & up-‐sell opportuni<es”
Opinion Mining
“ How do I gain ac<onable
insights from market & customer interac<ons
across channels? ”
Auto-‐Categoriza<on
“ As a retailer, how do I display
categorized lis<ngs in the most efficient manner? “
Intelligent Agents
“ With so much
informa<on overload, how do I transform
the effec<veness
of my knowledge workers? “
Customer Testimonial
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“We partnered with MeshLabs because of their unique ability to connect to and integrate all types of data and content from our communi<es. This allows us to bring game changing analy<cs and repor<ng to our clients enabling them to discover new insights to refine messaging, cra0 an innova<on strategy, and improve customer loyalty.”
THOMAS FINKLE CEO, Think Passenger, Inc.
Passenger is a leader in providing Market Research Online Communi1es PlaKorm
Our Product – eZi CORE™ Text Analytics Engine
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MeshLabs eZi CORE ™
eZi Semantic Search ™
eZi Reco ™
eZi Connectors ™ and CrawlersMicrosoft SharePoint, Outlook, Alfresco
Enterprise ContentWeb Content
eZi Sentiment Analyzer ™
Entity Extractor
POS Tagging Classifier Clustering Rules
EngineInference /Reasoner
Unified Semantic Index / Triple Store
Search Interface Dashboards APIs
Custom Solutions
• On-‐Premise • SaaS • API
Core Capabilities
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ü Data Acquisi<on and Inges<on ü Text Prepara<on ü Named En<ty Extrac<on ü Auto-‐Categoriza<on ü Feature Extrac<on ü Sen<ment Analysis ü Summariza<on ü Recommenda<on ü Faceted Search ü Dashboard & Repor<ng
Data Acquisition and Ingestion
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• File System • SharePoint • Alfresco • Web Crawler
• TwiUer • Facebook • Blogs • YouTube • Discussion Forums
• Yahoo Answers • Hadoop File System (S3,
HDFS etc.) • Databases (any JDBC
compliant database)
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Out-of-the-box Taxonomies
Airline Industry Automobile Industry Banking
Company -‐ Industry
Classifica<on
Computers and Laptops
Corporate Social Responsibility Cosme<cs Customer Service
-‐ Generic
Hotels Human Resources
-‐ Voice of Employee
Product-‐Category Classifica<on Real Estate
Retail Smart Phones and Tablets Telecom Travel
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Dashboards & Reports
Sentiment Analysis
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• Feature-‐Based Sen<ment Analysis supported • Lexicon based analysis
§ Per-‐domain lexicon supported • Uses deep parsing to
§ Iden<fy features § Associa<on of nega<on and suppor<ng words
• Mul<ple levels of sen<ment scoring supported • Sen<ment Analysis done at sentence fragment scope • Weighted rollup of sen<ment score provides overall
view
Feature Detection
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• Features: Extrac<on of Context Relevant Nouns / Noun Phrases ü Noun Phrase Extrac<on ü Deep Parsing and Lexical Chaining ü Sen<ment Scoring at Feature-‐level
“The coffee was bad, but the sandwich was good.” • Featureless Sen<ment Score – Neutral • Featured-‐based:
ü Overall Sen<ment – Neutral ü Coffee – Nega<ve ü Sandwich -‐ Posi<ve
Contact Us
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[email protected] www.meshlabsinc.com
@meshlabs linkedin.com/company/meshlabs facebook.com/meshlabs
USA: 1-‐602-‐617-‐9370 | India: 91-‐9986004572