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<< Goal - Data Science
• Live Session#Business Case Study


HOW FLIPKART'S HELPING SMALL BUSINESS TO GROW USING NLP

Did you ever wonder how e-commerce sites such as Flipkart help small businesses
shine.




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DESCRIPTION



Have you ever wondered how e-commerce sites such as Flipkart help small
businesses shine?. Which technology is Flipkart using to execute this process?




The e-commerce firm has collected consumer data since 2007 from transactions on
the website and app. On an average day, Flipkart collects over 10 – 15 terabytes
of data from nearly 120 million users. 




For popular brands like Flipkart, natural language processing and artificial
intelligence is the way forward. Flipkart, which recently garnered attention for
selling its 77% stake to brick-and-mortar heavyweight Walmart for a steep US$16
billion, leverages artificial intelligence to determine the potential customers
based on product reviews. 




Customer Feedbacks play a pivotal role in improvisation of product and services
deciding the direction of business growth. Reading tons of product reviews and
finding the goods and bads of a product seem daunting but thanks to Machine
Learning through which Flipkart can process all reviews at once and provide
necessary actions to the small businesses on where to improve and what people
are liking about their product.




Using artificial intelligence to analyze tons of data can unveil profound
insights on consumer tastes and preferences – a strategy that has played a
crucial role in Flipkart’s growth since 2007. Flipkart analyzes these insights
to take actions in improvising its online shopping experience, including which
product it offers as well as where it should focus its innovation efforts.




Better product recommendations — Flipkart correlates user behaviour with
feedback for better product recommendations on specific product aspects can help
them better understand what users are looking for and recommend suitable
products.

In this session, we’ll talk about how Flipkart uses NLP to review product
feedbacks and suggest improvisations to sellers.





WHAT YOU’LL LEARN IN THIS SESSION?




 1. How product review helped Flipkart to understand user behaviour?
 2. What is Natural language processing?
 3. Natural language processing applications
 4. Introduction to Topic Modeling
 5. LDA algorithm explained





WHO CAN ATTEND THIS SESSION?

 

 1. Anyone who is curious about data science & deep learning for Natural
    language processing.
 2. If you are into the business & marketing world - learn to use NLP to gain
    insight into customers & products. 
 3. Students who are looking for a really good project on Natural Language
    Processing with Python.
 4. Python developers interested in learning how to use Natural Language
    Processing in AI.

So, let’s get started!!





HOW PRODUCT REVIEWS HELPED FLIPKART TO UNDERSTAND USER BEHAVIOUR?




Product reviews is an essential part of an online store like Flipkart’s branding
and marketing. They help to build trust and loyalty and typically describe what
sets your product apart from others. Savvy shoppers almost never purchase a
product without knowing how it’s going to work for them. The more reviews a
platform has, the more convinced a user will be that he/she is making the right
decision.




Online reviews are very important to e-commerce businesses because they
ultimately increase sales by giving the consumers the information they need to
make the decision to purchase the product. One other important factor in
elevating the reputation, standard, and evaluation of an e-commerce store is
product rating. 





WHAT IS NATURAL LANGUAGE PROCESSING?




Natural Language Processing (NLP) helps machines “read” text by simulating the
human ability to understand language. It is a field of Artificial Intelligence
that gives machines the ability to read, understand and derive meaning from
human languages.





USE OF NATURAL LANGUAGE PROCESSING IN AI




NLP techniques do a semantic analysis of millions of user reviews and extract
useful information out of them. The models make use of sophisticated natural
language processing algorithms for aspect identification, text extraction,
sentiment classification, and then aggregation. First Natural Language
Processing steps in the process is key phrase extraction in which they identify
patterns of language containing the critical information expressed by the
customer.




In the next step, they use concepts of Topic Modeling and Phrase-to-Phrase
similarity to determine the product dimension specifically being commented on.
Following this step, they use an aspect-based sentiment scoring approach to
convert the selected linguistic phrases into a score that can be aggregated
across all the customers.





NATURAL LANGUAGE PROCESSING APPLICATIONS –

 

Natural Language Processing with python may not be known widely like Data
Science and Machine Learning. But we use natural language examples in everyday
life. Some of the real-world examples are –




 1. Text Classification
 2. Sentiment Analysis
 3. Machine Translation
 4. Automatic Summarization
 5. Question Answering





INTRODUCTION TO TOPIC MODELING




Topic modeling is an unsupervised machine learning technique that automatically
identifies topics present in a text object and derive hidden patterns exhibited
by a text document. Topics are important words that are enough to suffice the
meaning of the complete sentence.




Since topic modeling doesn’t require training, it’s a quick and easy way to
start analyzing your data. However, you can’t guarantee you’ll receive accurate
results, which is why many businesses opt to invest time training a topic
classification model.





HOW DOES TOPIC MODELING WORK?




Topic modeling in python involves counting words and grouping similar word
patterns to infer topics within unstructured data. Let’s take the example of
Flipkart where you might want to know what customers are saying about a
particular product from x seller. Instead of spending hours to find out the
best-reviewed product through heaps of feedback, you can analyze them with a
topic modeling algorithm.




By detecting patterns such as word frequency and distance between words, a topic
model clusters feedback that is similar, and words and expressions that appear
most often. With this information, you can quickly deduce what each set of texts
are talking about.





LDA ALGORITHM EXPLAINED –




LDA suppose documents are produced from a mixture of topics. These topics then
generate words as per probability distribution. Provided a dataset of documents,
LDA backtracks and tries to figure out which topics may create those documents
in the first place. The purpose of LDA is to map each document in our corpus to
a set of topics that covers a good deal of the words in the document.




The main difference between LSA and LDA is that the LDA algorithm pre assumes
that the distribution of topics in a document and the distribution of words in
topics are Dirichlet distributions. LSA does not assume any distribution and
therefore, leads to more opaque vector representations of topics and documents.



HOW FLIPKART'S HELPING SMALL BUSINESS TO GROW USING NLP



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WHAT OUR LEARNERS HAVE TO SAY ABOUT US!

GOWKANAPALLI

UNIVERSITY INSTITUTE OF EMERGING TECHNOLOGIES (UIET), GHARUAN

5

I loved this webinar. I learnt many things through this. The session was
interactive. But it would have been good if we were alllowed to speak through
microphone. I am awaiting more sessions like this. Thank you.

SAMCHRISTY

AURORA'S TECHNOLOGICAL & MANAGEMENT ACADEMY

5

Session was little fast but was great. Very nice explaination as expected like
80:20 principal explained terms in short but that much only info was expected.
Thank u for such a nice session.

BHOLARAM

PRIYADARSHNI INSTITUTE OF TECHNOLOGY & MANEGEMENT

4

It was an excellent session. Enjoyed it to the core. Gave me a new view towards
career opurtunities.


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