What Is Text Analytics And Why It Matters In Data Analysis In 2020

Text analytics is a data analytics technique used to derive meaning and sentiment from text. The text can be from comments, reviews, or social media posts. The derivation of the text is used to gain insights to customer opinions, user feedback, and more valuable customer information.

Text analytics is simply a method of translating a large amount of unstructured data or data into information that’s easily understood and analyzed.

For gaining insights to user feedback, text analytics is often used to dissect the content in open text fields using categorisation, clustering, pattern recognition, tagging and visualisation.

Overall, text analytics are useful for the following three reasons:

  • Market research. You can use text analytics to extract and interpret how customers view a product or service; you can find out if customers think a product or service is overpriced, has certain malfunctions, or other insights.
  • Customer feedback. Analyzing customer comments or reviews lets you perform sentiment analysis and understand your customers’ attitude towards your brand. 
  • Emerging trends. Gaining insights to what customers are saying about a product or service or an industry and specific product features users enjoy or complain about

Here are four common text analytics techniques that you can use to perform text analytics:

1. Text Frequency

This is text analytics in its simplest form, whereby the topics (e.g. pricing, service, account, etc.) are counted and brought to the top based on the frequency with which they are mentioned. This is ideal for quickly identifying common topics and issues that arise among your visitors.

2. Sentiment Analysis

Having applied the previous techniques, you now know how frequently certain words occur and how they are grouped, but is this feedback positive, negative or neutral? Luckily for you, your customers are likely to provide you with feedback on topics they feel strongly about, so with the right tool in place, gauging sentiment shouldn’t be an issue.

Sentiment analytics (or Opinion Mining) is a field within Natural Language Processing (NLP) that enables users to gauge the severity of the feedback based on positive, negative and neutral word usage as well as the sentiment associated with commonly used words.

Additionally, with some tools you can analyse strictly negative and positive sentiments and most commonly used words associated with those sentiments.

3. Word Pairing

Often times a group of words can provide you with more insight than just one word alone. For example, when words like “costs”, “expensive” and “monthly” are grouped together, you can safely conclude that there are many customers that think the monthly costs for one of your products or services are too expensive. But to take a closer look you can always open the individual comments.

4. Word Cloud

Word Cloud

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