Text mining and sentiment analysis

MX Bites / March 3, 2022

Text mining, also known as text analytics, is a process of extracting value from large quantities of unstructured text data and transforming it into useful business intelligence. Most businesses have a huge amount of text-based data in an unstructured format, particularly across social media. 

While the text is structured to make sense to a human it is unstructured from an analytics perspective because it doesn’t fit neatly into the database. The only structured text part of text traditionally was the name of the documents, date of creation, and name of the creator. 

Today, text mining is capable of telling us things we didn’t know, and even more importantly, had no way of knowing before. For example, now we can assess text data for commercially relevant patterns such as increase or decrease in customer satisfaction, new insights that could lead to product and services tweaking. There are numerous reasons why your business might require the use of text mingin. There are five main text mining techniques: 

    • Text categorization; 
    • Text clustering: 
    • Concept extraction; 
    • Sentiment analysis; 
    • Data summarization; 

At Hoick we conduct all five in an automated fashion and with an advanced data story that unveils insights at the touch of the button. However, today we will expand on sentiment analysis, what it means, how it works, and why it is important to any business, irrespective of size.

What is it? 

Sentiment analysis is contextual mining of text which identifies and extracts subjective information in the source material and helps a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. The basic aim of sentiment analysis is to determine the attitude of an individual or group regarding a particular topic or overall context. It may be a judgment, evaluation, or emotional context. 

Sentiment analysis enables the business to distinguish if stakeholders feel positive, negatively, or neutrally about products, services, businesses, and brands. 

When do I use it? 

You would use sentiment analysis when you want to find out stakeholders’ opinions or feedback. Say you have a lot of text data from your customers that came from emails, surveys, social media, etc. There are several million data points in large text analysis that won’t make sense to a human eye if not analyzed, summarized, and made sense out of it. 

Therefore, the duality of sentiment can be applied to your customer feedback text to establish what they feel about you. CustomerMX, for example, enables organizations to monitor, respond and improve the main points along a customer journey and incorporate customer feedback into every decision, all in real-time. 

What business questions is it helping me to answer? 
    • How positive or negative do our customers feel about our brand? 
    • What product/service is the most important to our customers? 
    • What are key trends and patterns based on real-time data? 
    • Are we improving products and services on a daily, monthly, and yearly basis? 
    • What is the perception of our employment brand? 
Practical example

As most of our readers already know, we aim to ‘walk the talk’ most of the time, therefore in this read we also point out a practical example. Our team designed a single, consolidated view of CX performance on a real-time dashabord that you can explore on this LINK

By analyzing data from various sources, we discovered what products and services have the biggest impact on hotel performance. All of the charts on the dashboard are interactive, meaning you can click on the chart to filter and mine particular text data. Alternatively, connect with us for a coffee so that we can explore more use cases together. 


Sentiment analytics is pretty funky stuff because it can bring certain things to our attention we didn’t know and had no way of understanding in the past. This makes it appealing and sexy but make sure you are not just jumping on the bandwagon because it sounds like a useful thing to do. Like all analytics, it is only useful if there’s a lucratively viable reason for doing it. Make sure there is!

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