This is a great place to experiment and apply Natural Language Processing (NLP) techniques. This article will help you understand the significance of harnessing online product reviews with the help of Topic Modeling..Please go through the below articles in case you need a quick refresher on Topic Modeling: Introduction to Topic Modeling using LSA Beginners Guide to Topic Modeling in Python Table of Contents Importance of Online Reviews Problem Statement Why Topic Modeling for this task?.Python Implementation Reading the data Data preprocessing Building an LDA model Topics Visualization Other methods to leverage online reviews What’s Next?.How we can analyze a large number of online reviews using Natural Language Processing (NLP)?.This system will serve two purposes: Enable consumers to quickly extract the key topics covered by the reviews without having to go through all of them Help the sellers/retailers get consumer feedback in the form of topics (extracted from the consumer reviews) To solve this task, we will use the concept of Topic Modeling (LDA) on Amazon Automotive Review data..As the name suggests, Topic Modeling is a process to automatically identify topics present in a text object and to derive hidden patterns exhibited by a text corpus..Topic Models are very useful for multiple purposes, including: Document clustering Organizing large blocks of textual data Information retrieval from unstructured text Feature selection A good topic model, when trained on some text about the stock market, should result in topics like “bid”, “trading”, “dividend”, “exchange”, etc..The below image illustrates how a typical topic model works: In our case, instead of text documents, we have thousands of online product reviews for the items listed under the ‘Automotive’ category..Our aim here is to extract a certain number of groups of important words from the reviews..These groups of words are basically the topics which would help in ascertaining what the consumers are actually talking about in the reviews..Here we’ll work on the problem statement defined above to extract useful topics from our online reviews dataset using the concept of Latent Dirichlet Allocation (LDA)..2/3 reviewText – text of the review overall – rating of the product summary – summary of the review unixReviewTime – time of the review (unix time) reviewTime – time of the review (raw) For the scope of our analysis and this article, we will be using only the reviews column, i.e., reviewText.. Data Preprocessing Data preprocessing and cleaning is an important step before any text mining task, in this step, we will remove the punctuations, stopwords and normalize the reviews as much as possible.. More details