Objective: For experience brands wanting to manage reputation managing and learning from data on review sites that is viewed by millions making it very important. However, collecting external data, cleaning it and making meaning out of the same is a highly difficult and expensive exercise.

External data offers critical insights into brand perception and competitive scenarios

Solution:

We scrapped over 10 million customer reviews for more than 100000 restaurants across India. We focused on the following questions for analyzing the reviews;

  • What are the keywords & topics of discussion across the comments? and derived using topic modeling techniques
  • What elements of the restaurant would they want improved? –Service, staff behavior, ambiance etc. by using  text classification techniques
  • What is the overall mood of the customers visiting the restaurant? by using   Sentiment analysis 

Impact

This project involved a beautiful concoction of text and structured data, which enriched the data manifold. We used advanced techniques of text analytics like, machine learning for topics classification, sentiment analysis, keyword parsing  and relations extraction

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MINING CUSTOMER REVIEWS TO UNDERSTAND CUSTOMER BETTER

Venugopal Manneni


A doctor in statistics from Osmania University. I have been working in the fields of Analytics and research for the last 15 years. My expertise is to architecting the solutions for the data driven problems using statistical methods, Machine Learning and deep learning algorithms for both structured and unstructured data. In these fields I’ve also published papers. I love to play cricket and badminton.


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