DataScience.com has released its Voice of the Customer Playbook, a comprehensive educational resource designed to help you understand feedback from your customers with natural language processing (NLP).

Playbooks, or comprehensive collections of instructional content, can be invaluable resources for data scientists. Containing topic-specific content, code libraries, vetted technical articles, notebooks, and more, each of our Playbooks tackles a specific business challenge with an advanced algorithm or data model. The Voice of Customer Playbook will be a valuable addition for data science teams looking to understand customers and boost ROI with the help of natural language processing.

But before we delve into the details of the Playbook and the value it will provide DataScience.com customers, let’s review what natural language processing is and why it’s important.

Natural Language Processing

Natural language processing
is a technique for programming systems to analyze and comprehend large amounts of text or audio. This is important because nearly every company is now collecting customer feedback from call centers, online reviews, social media, and emails. It could take hours or days to manually examine all of these sources for relevant and useful information.  Employing NLP techniques is an effective and efficient way to gauge customer satisfaction and discover product issues, ensuring that your company can quickly implement strategies to improve sentiment and reduce churn, among other initiatives.

Word Embeddings: A Natural Language Processing Crash Course

The primary article featured in the of the Voice of the Customer Playbook is DataScience.com Senior Data Scientist Ruslana Dalinina’s tutorial Word Embeddings: A Natural Language Processing Crash Course. Word embedding is a NLP technique that enables you to categorize semantic similarities between words based on their distributional properties based on large sets of language data. In her article, Dalinina explains the reasoning behind word embeddings and demonstrates how to use these techniques to create clusters of similar words using data from 500,000 Amazon reviews of food.  

By following Dalinina’s tutorial, readers are better able to categorize food types into different categories. For example, let’s say someone is interested in looking up reviews for high-protein snacks, but not protein supplements. By following Dalinina’s tutorial and inputting the relevant criteria into the model (such as protein snacks), readers can see that words such as “chips,” “bars,” “peanuts,” “nut” and terms like “healthy” live in the same word cluster as “snack” and “protein,” but not in the same cluster as “supplements.”

This is significant because identifying similarities in text-based datasets is not as straightforward as identifying similarities in numerical datasets. When someone asks how close X is to Y in a numerical dataset, it is easy and intuitive to subtract and compare numbers. When we are evaluating a text-based dataset, word embedding provides a clear and concise way to establish similarities between words algorithmically. 

Real World Applications of Word Embedding

Let’s look at another scenario at the enterprise level where word embedding would play a significant role. Let’s say you’re a hospitality manager, and you want to analyze customer reviews from guests who have stayed at one of your hotels in order to gauge customer satisfaction. You then hire a team of data scientists to implement word embedding in order to gather which words are clustered together in a large dataset of customer reviews. If “bad” and “service” are frequently grouped together, that’s a red flag.

Once your team of data scientists have identified these related words, they can incorporate these word embeddings into a more sophisticated deep learning model to predict customer sentiment for future reviews. Executives can use these data-driven insights to implement strategies that ensure customer loyalty. 

Word embedding is a crucial NLP technique for data scientists to have in their toolkit today, as companies are inundated with more audio and text-based data than ever before. If that data is analyzed accurately, it can yield highly informative insights that have the potential to boost ROI.

Want access to Playbooks? Take the first step in becoming a DataScience.com Platform user and request a demo today.

Found this interesting? Check out these NLP articles:

Using Data Science to Summarize, Sort, and Deliver Hotel Reviews

Trends in Open Source Libraries in Natural Language Processing 

Jacqueline Berkman
Author
Jacqueline Berkman