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.

1. MeshLabs
Text Analytics
©
2013
MeshLabs
So0ware
Private
Limited
Confiden<al

2. About US
2
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

3. Text, Text, Everywhere…
3

4. 4
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

5. Text Analytics
5
Linguis<cs
Sta<s<cs
Seman<cs
powerful
technology
to
automa<cally…
Ingest
all
text
data/content
Extract
valuable
assets
Deliver
ac<onable
insights
1
2
3

6. How it Works
6
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

7. Key Use Cases
7
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?
“

8. Customer Testimonial
8
“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

9. Our Product –
eZi CORE™ Text Analytics Engine
9
MeshLabs eZi CORE ™
eZi Semantic
Search ™
eZi Reco ™
eZi Connectors ™ and Crawlers
Microsoft SharePoint, Outlook, Alfresco
Enterprise Content
Web Content
eZi Sentiment
Analyzer ™
Entity
Extractor
POS
Tagging Classifier Clustering
Rules
Engine
Inference /
Reasoner
Unified Semantic Index /
Triple Store
Search
Interface
Dashboards APIs
Custom
Solutions
• On-­‐Premise
• SaaS
• API

10. Core Capabilities
10
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

11. Data Acquisition and Ingestion
11
• 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)

12. 12
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

13. 13
Dashboards & Reports

14. Sentiment Analysis
14
• 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

15. Feature Detection
15
• 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

16. Contact Us
16
sales@meshlabsinc.com
www.meshlabsinc.com
@meshlabs
linkedin.com/company/meshlabs
facebook.com/meshlabs
USA:
1-­‐602-­‐617-­‐9370
|
India:
91-­‐9986004572