Check out how Text Analytics plays match maker. Using appropriate techniques text analytics can go a long way in reducing the manual work in the social & legal areas such as contract management, structured document decomposition, sentiment analysis and even in news summation and dating and job portals. Techniques like natural language processing, word2vec, deep learning, TF-IDF are used to get the best output.

1. The Match Maker: Text
Analytics at work
October 5, 2017

2. Slide 2
About AlgoAnalytics
Match Making: Problem Statement
Text Analytics: Keyword Match
Text Analytics: Use cases
Other text analytics work
Technologies

3. Slide 3
Aniruddha Pant
CEO and Founder of AlgoAnalytics
PhD, Control systems, University of
California at Berkeley, USA 2001
• 20+ years in application of advanced mathematical techniques
to academic and enterprise problems.
• Experience in application of machine learning to various
business problems.
• Experience in financial markets trading; Indian as well as global
markets.
Highlights
• Experience in cross-domain application of basic scientific
process.
• Research in areas ranging from biology to financial markets to
military applications.
• Close collaboration with premier educational institutes in India,
USA & Europe.
• Active involvement in startup ecosystem in India.
Expertise
• Vice President, Capital Metrics and Risk Solutions
• Head of Analytics Competency Center, Persistent Systems
• Scientist and Group Leader, Tata Consultancy Services
Prior Experience
• Work at the intersection of mathematics and other
domains
• Harness data to provide insight and solutions to our
clients
Analytics Consultancy
• +30 data scientists with experience in mathematics
and engineering
• Team strengths include ability to deal with
structured/ unstructured data, classical ML as well as
deep learning using cutting edge methodologies
Led by Aniruddha Pant
• Develop advanced mathematical models or solutions
for a wide range of industries:
• Financial services, Retail, economics, healthcare,
BFSI, telecom, …
Expertise in Mathematics and Computer
Science
• Work closely with domain experts – either from the
clients side or our own – to effectively model the
problem to be solved
Working with Domain Specialists
About AlgoAnalytics

4. Slide 4
AlgoAnalytics - One Stop AI Shop
Aniruddha Pant
CEO and Founder of AlgoAnalytics
•We use structured data to
design our predictive analytics
solutions like churn,
recommender sys
•We use techniques like
clustering, Recurrent Neural
Networks,
Structured
Data
•We use text data analytics for
designing solutions like sentiment
analysis, news summarization and
many more
•We use techniques like natural
languageprocessing, word2vec,
deep learning, TF-IDF
Text Data
•Image data is used for predicting
existence of particular
pathology, image recognition
and many others
•We use techniques like deep
learning – convolutional neural
network, artificial neural
networks and technologies like
TensorFlow
Image Data
•We use sound data to design factory
solutions like air leakage detection,
identification of empty and loaded
strokes from press data, engine-
compressor fault detection
•We use techniques like deep
learning
Sound Data
BFSI
•Dormancy Analysis
•Recommender System
•Credit/Collection Score
Retail
•Churn Analysis
•Recommender System
•Image Analytics
Healthcare
•Medical Image Diagnostics
•Work flow optimization
•Cash flow forecasting
Socio-Legal
•Contracts Management
•Structured Document decomposition
•Document similarity & keyword match in text
analytics
Internet of Things
•Predictive in ovens
•Air leakage detection
•Engine/compressor fault detection
Others
•Algorithmic trading strategies
•Risk sensing – network theory
•Network failure model

5. Slide 5
Problem Statement: Match Making at Work
Numerical Data: Gender,
age, salary, locality,
experience, education level
Text Data: Industry, Role,
Skill set, specialization
How do we Match the best Job Seeker for a given Job Description?
Features used to represent Job Description data and Job Seekers data are
mentioned below :
Job Description Data Job Seekers Data
Campaign ID Jobseeker ID
Industry Industry
Function Function
Gender Gender
Minimum Age Age
Minimum Salary Salary
Skills Skills
Locations Current Locality
Minimum Experience Experience
Education Education
Specialization Specialization
Profile status
DataSet

6. Slide 6
The Techniques Employed
For a given Job Description similarity with various Job seeker’s
Profiles
Text Processing Techniques:
Word2Vec, tf-idf, Glove, are used
for matching text data
Vector Distance Calculation

7. Slide 7
The How?
To find best matching search results fielded through text data or in conjunction
with query and analysis of fielded numerical data
Word2Vec:
•Word2vec is a group of related models
that are used to produce word
embeddings.
•These models are shallow, two-layer
neural networks that are trained to
reconstruct linguistic contexts of words.
• Word2vec takes as its input a large
corpus of text and produces a vector
space, typically of several hundred
dimensions, with each unique word in
the corpus being assigned a
corresponding vector in the space.

8. Slide 8
Text Analytics: Use Cases
Finance: Search, compliance, money laundering
Insurance: Sentiments of customer, claim adjuster notes
Media: Social media and audio video content analytics
Retail: based on customer feedback brand analytics
Legal: Keyword Search, Contract Management
Healthcare: Medical records
Social Spheres: Caption generation to help the visually-disabled,
Marriage Matchmaking, dating services
Text Analytics can be used over a variety of domains and solve many manual oriented problems

9. Slide 9
Twitter Analytics
- Identify, process
and group
together relevant
tweets using
machine learning
methods
News Analytics
- Access, identify
and analyze
relevant news
article given a
topic
- News
summarization
App Development
- Download,
analyze twitter
feeds of stocks to
get sentiment and
topic detection
Multi-language
Sentiment
Analysis
- Model can be
used to get similar
words.
- Trained model
can learn
proximity of words
Topic Summary,
Concept Detection
- Keyword
extraction
-summary
extraction
- Topic detection
-Words
Importance

10. Slide 10
Technology:

11. Interested in knowing more?
Contact us: info@algoanalytics.com
Website: http://algoanalytics.com/
October 5, 2017