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 I learned in this session?
- How product review helped Flipkart to understand user behaviour?
- What is Natural language processing?
- Natural language processing applications
- Introduction to Topic Modeling
- LDA algorithm explained
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
- Text Classification
- Sentiment Analysis
- Machine Translation
- Automatic Summarization
- 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.
