The Role of NLP in Customer Review Analysis

With the plethora of customers’ insights and opinions about products and services on the web, NLP technology can be used to easily process and analyze these reviews at scale, extracting useful information and sentiment from the data.

The Role of NLP in Customer Review Analysis

Every successful business thrives on customer feedback. What users are saying and not saying about a product or service (yours or competitors') speaks volumes about what is being done right or wrong, and smart business owners tap into this in order to create products or improve their services in a way that would optimally serve their prospects and consumers.

Thankfully, with the aid of business intelligence solutions like review and sentiment analysis, businesses can better understand customers’ opinions, pain points and preferences at scale. Businesses now have the means to sift through vast amounts of consumer data in search of intelligent data that will enhance product development, operational effectiveness and many other aspects of the business. These help to improve customers' experiences, and foster customer loyalty and business growth.

Customers generate large volumes of data in today’s digital market. Those data can be gotten from social media, online reviews, customer support, and product/brand forums or communities. These large volumes of data become impossible to manually go through and analyze. This is where Natural Language Processing technologies come into play.

What Are Natural Language Processing Technologies?

Natural Language Processing (NLP) is an aspect of AI that helps ensure interaction between computers and human language. It involves the ability of computers to understand, interpret, and generate human language in a way that is meaningful and useful. NLP technology enables machines to analyze and derive insights from text and speech data.

Woven Insights dashboard showing sentiment analysis for SKIMS Bra
Source: Consumer Insights solution by Woven Insights

With the plethora of customers’ insights and opinions about products and services on the web, NLP technology can be used to process and analyze these reviews at scale, extracting useful information and sentiment from the data.

Benefits of NLP In Review Analysis

Rapid data processing

Fear not unstructured and large-volume data, for NLP comes to the rescue in extraction and interpretation. Through machine learning algorithms, statistical models, and linguistic rules, natural language processing technologies automatically process and analyze unstructured text data to determine customer sentiment and identify recurring trends in reviews in an automated manner.

NLP helps classify and trace reviews according to geographical locations, demographics of consumers, organizations, and even the products mentioned in the reviews.

Theme categorization

When engaging in review analysis there are certain themes that you should be looking out for, whether positive or negative comments or opinions. With a billions of reviews out there, manually identifying and categorizing each review according to the themes you are looking for is going to be a herculean task.

What Natural Language Processing technology does is to understand the context of each review accurately through the keywords used by the customer, employ Latent Dirichlet Allocation to garner all the themes present in each review, and utilize an automated categorization of each review into meaningful categories.

Keyword analysis

It isn’t enough to categorize each review according to themes and represent them as either negative or positive, it is very important to know the source of each review and the factors behind them.

"What are the common keywords associated with the negative, positive and mixed reviews?"

Woven Insights dashboard showing sentiment and keyword analysis for SKIMS Bra
Source: Consumer Insights solution by Woven Insights

The insights from this would help you look beyond the stats and really delve into the pain points and pleasure points of the customers. For example, if 23 out of 29 positive reviews have keywords like ‘portable’ or ‘easy to hold’, one can identify the features that make customers like the usage of product Y. NLP helps to break down the text in a way that keywords can be extracted, the context can be understood, and similar words can be grouped together from the data set.

Conclusion

Customer review analysis holds immense value for businesses seeking to understand and improve their products and services.

With the help of Natural Language Processing technologies as found in solutions like Woven Insights and Yotpo, ecommerce businesses can easily overcome the challenges of manual analysis and gain rapid, accurate, and comprehensive insights from customer reviews.

By harnessing the potential of NLP, businesses can make data-driven decisions, enhance customer experiences, and stay ahead in the competitive market.