Customer Review Analysis


Use Cases

Understanding most discussed topics among customers. It may help in identifying the greatest concerns faced by them.

Topic Modelling


Sentiment Analysis

Understanding online customers' sentiments towards the products. Sentiments could be positive, negative or neutral.


The E-commerce client wanted to know more about common concerns faced by their customers on regular-basis. They provided big dataset of all customers' reviews for last 1 year. In addition, overall customers' sentiments needed to be analysed.


The analysis report was based on two models: Topic Model and Sentiment Analysis model.  

Topic Model:


Latent Dirichlet Allocation or LDA model is a kind of Generative Probabilistic Model consisting of a set of composites (documents) made up of parts (words). It was used to understand different abstract topics most frequently discussed by their online customers on the website.


Sentiment Analysis Model:


Customer sentiments could be positive, negative or neutral. We tried to classify all individual comments into either of these three categories. As part of the analysis different supervised learning algorithms were evaluated and the model was chosen based on accuracy and reliability. 


Tools & Technologies

Python Libraries & Frameworks

  • Anaconda Distribution

  • Python 3.x

  • CSV data format

  • SciKit Learn

  • numpy

  • pandas

  • nltk

  • gensim

  • pyLDAvis