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These are important questions customers need to ask before splurging their money. 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:. A few days back, I took the e-commerce plunge and purchased a smartphone online. It was well within my budget, and it had an above decent rating of 4.

Unfortunately, it turned out to be a bad decision as the battery backup was well below par. Ratings alone do not give a complete picture of the products we wish to purchase, as I found to my detriment. But then an interesting problem comes up. What if the number of reviews is in the hundreds or thousands? And this is where natural language processing comes up trumps. A problem statement is the seed from which your analysis blooms. Therefore, it is really important to have a solid, clear and well-defined problem statement. Online product reviews are a great source of information for consumers.

However, since these online reviews are quite often overwhelming in terms of numbers and information, an intelligent system, capable of finding key insights topics from these reviews, will be of great help for both the consumers and the sellers. This system will serve two purposes:. Similar datasets for other categories of products can be found here. 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:. The below image illustrates how a typical topic model works:. 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.

Note: As I mentioned in the introduction, I highly recommend going through this article to understand what LDA is and how it works.

For the scope of our analysis and this article, we will be using only the reviews column, i. After every preprocessing step, it is a good practice to check the most frequent words in the data. These words are not so important for our task and they do not tell any story. We can see some improvement here.


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It reduces any given word to its base form thereby reducing multiple forms of a word to a single word. As you can see, we have not just lemmatized the words but also filtered only nouns and adjectives.