Text analytics, sentiment detection etc. form a very important part of NLP. With huge data available today, making sense of the dara, detecting latent patterns, tagging documents etc. has become an important part of language modelling. Let us see how Azure Cognitive services enable us to detect document sentiments, Named entities, Key phrases, contextual information like geo spatial indices etc.

1. NLP : TEXT ANALYTICS WITH
AZURE COGNTIVE SERVICES
PRIYANKA H SHAH

2. I am…
 Head of Innovation and Technology.
 AI/ML solution architect and Big data solution engineer.
 Full stack developer.
 Passionate about new technologies, love to code, blog / talk about AI, ML.NET,
Microsoft technology stack.
 Environment enthusiast
 Twitter: @fuzzymind1

3. THE DATA
EXPLOSION
PROBLEM
Huge data available:
Text documents
Anomly / Error logs
Earnings call transcripts
Annual Reports
Potential of data is untapped
Latent patterns unexplored
Quick ways to leverage the data potential?

4. REAL TIME
BUSINESS
CASE
Built a document text
search with Elastic Search
Search efficiency for term
search, phrase search,
fuzzy search was quite
good
• Driverless cars -> Self driving cars, robotic cars
• Lithium -> Other metals/ Utilities sector related
information, dry cell or related industries
Related terms search?
• like were you looking for <term> OR
• People who searched this report, also searched
<list of related reports>
Recommend search
terms for sematic
searches

5. Other constraints
550,000 call reports
available
Distributed across 11
sectors
New call reports added
daily
Impossible to go
through each document
and identify the “tag”
words
How to tag newly
uploaded call reports?
How to establish word
semantic relationships /
build a word taxonomy
for all documents

6. AZURE TEXT ANALYTICS COGNITIVE
SERVICE
•sentiment analysis,
•opinion mining,
•key phrase extraction,
•language detection,
•named entity recognition.
cloud-based
service that
provides Natural
Language
Processing (NLP)
features for text
mining and text
analysis

7. FEATURES OF
TEXT
ANALYTICS
IDENTIFY AND
CATEGORIZE
IMPORTANT CONCEPTS
BETTER UNDERSTAND
CUSTOMER PERCEPTION
DETECT LANGUAGE OF
YOUR TEXT
EXTRACT KEY PHRASES
IN UNSTRUCTURED
TEXT
PROCESS
UNSTRUCTURED
MEDICAL DATA
DEPLOY ANYWHERE,
FROM THE CLOUD TO
THE EDGE

8. DEMO

9. COMPLETE SOLUTION ARCHITECTURE
Identify topics
using LDA
Build word
taxonomy using
the topics and
the words
occurring in
each topic
For example :
Insurance,
premium, policy,
claim,
reinsurance can
be added as
synonyms
Use Azure
Cognitive text
Analytics
services to get:
Sentiment for each
document
Named Entity
recognition
Key phrases
Contextual
information about
NERs
Use the text
Analytics
information to
tag documents
Feed the tags
and synonyms
to Azure
Cognitive search
using Synonyms
API
Iteratively build
a more
sophisticated
taxonomy. Keep
refining every
month for new
reports

10. DETAILED
SESSION LINK:
• https://www.youtube.com/watch?v=S
MfN4eSS0PA

11. THANK YOU