Introducing Text Analytics 2.0

Text analytics analyses unstructured text data and turns it into insights, trends and patterns

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Introducing Text Analytics 2.0
Data & AnalyticsInsights

Last Edited: August 31, 2022

Sandra Radlovački

Sandra Radlovački

In most cases, the process involves machine learning, sentiment analysis and natural language processing techniques to help organizations uncover valuable nuggets of data and improve their performance.

With more and more consumers opting for text messages and written communication in general, rather than picking up the phone, the ROI on text analytics tools increases.

When applied to a contact center environment, text analytics can help businesses process large volumes of written customer interactions, including reviews and other types of textual data.

As more unstructured data becomes available than ever before, companies can extract every bit of customer data, and at the same time, assess business performance and keep in the loop with how customers are feeling about their brand.

Those brands that want to level up their conversation intelligence game need to explore lesser-known applications of text analytics.

Untapped potential of text analytics

According to, text analytics can be applied to non-textual forms of data, including call transcripts, chat conversations, meeting notes, and messages of contextual nature that need to be analyzed in relationship with other forms of data.

By making full use of call transcripts and speech-to-text data, businesses can double down on text analytics tools, partially or entirely replacing the need for speech analytics solutions.

With the right tools, every spoken word can become text which becomes unstructured data that may reveal valuable insights and point businesses in the right direction.

If your organization is already ingesting every piece of data through non-textual forms of data, it is important to ensure that the NLP models and techniques are adapted to the type of data you understand.

For example, machine learning technique such as topic modelling needs a different approach if it’s applied to video call or Twitter feed.

Also called ‘unsupervised’ machine learning technique, topic modelling identifies the topics of a set of text clusters by detecting patterns and recurring words. Depending on the type of text it is analyzing, topic modeling can group similar texts according to similar things.

Similarly, sentiment analysis also had different applications. While it’s a great tool for analyzing common customer calls, sentiment analysis needs to be contextual in a sales call, separating relevant sales discussion from the small talk.

Advanced sentiment analysis models are able to separate biased bits of speech and focus on the valuable insights in a conversation.

In an age where every conversation matters, text analytics is just one part of a strategy puzzle for better understanding customers. uses the power of pre-trained machine learning to find and analyze the nuances of human conversations.

Eager to learn more about the power of text analytics? Reach out to by visiting this page:


Machine LearningNatural Language Processing

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