Things such as small and little speak potentially to a sizing issue where some clothes are not true to fit. We have lost of positive words but also some words that might be worth looking into. Tada □! the top-most used words that describes how our customers are talking about our products. The function below takes each review and determines the POS tag for each word an important distinction because we get the context of each word in the sentence, and as we saw above, this makes a big difference in which POS tag is associated. ![]() You may have a perfect categorization of your products in a database, but what if you don't at the granular level you need? For this example, we will use the dataset of Women's E-Commerce Clothing Reviews on Kaggle.Īfter importing the dataset, we can create a new DataFrame of all the words and their POS tag. One of the things you might want to identify is all the products that people are talking about. Let's say you have a collection of customer reviews. Text for your analysis can come from survey responses, support tickets, Facebook comments, Tweets, chat conversations, emails, call transcripts, and online reviews 8. From Sentiment Analysis to Topic Modeling, one method you can use is Part of Speech tagging to narrow what customers are talking about and how they talk about your products and services. There are many ways to perform a VOC analysis. This type of analysis is called Voice of Customer Analysis or VOC 8. One of the most common tasks performed with NLP is analyzing customer feedback from various sources and determining what customers are talking about for your product or service. Voice of Customer Analysis with Parts of Speech TextBlob is great when you want simplicity across several NLP tasks, and Spacy when you want one of the most robust NLP libraries around.Ĭheck out this great Series NLTK with Python for Natural Language from. I believe you should start with NLTK to understand how it works, especially since it has so much robust support of different corpora. We see here that Spacy correctly tagged all of our words, and it identified Please like an Interjection 7 as opposed to a Verb, which is more accurate and also identified Book as a Verb in the first sentence.Įach of these libraries has its pros and cons. Start by importing all the needed libraries. We'll do the absolute basics for each and compare the results. Let's start with some simple examples of POS tagging with three common Python libraries: NLTK 4, TextBlob 5, and Spacy 6. These are not always considered POS but are often included in POS tagging libraries.
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