Text Analytics

Hi, exploring around? I’m Conviner, your call center terminology assistant, ready to help you learn more about contact centers. 

Did you know? In 2022, the software component of the text analytics market accounted for 51.6% of the total market value.

1. What is Text Analytics?

Text analytics is the process of using AI and natural language processing (NLP) to extract meaningful insights from unstructured text data. It helps businesses analyze customer feedback, reviews, and conversations to understand sentiment, intent, and trends.

2. What is an example of text analysis?

An example of text analysis is sentiment analysis, where AI models evaluate text, like customer reviews—to determine if the sentiment is positive, negative, or neutral. Other examples include topic detection, keyword extraction, and intent analysis.

3. What is the purpose of text analysis?

The purpose of text analysis is to transform large volumes of textual data into actionable insights. It enables better decision-making, enhances customer experience, and identifies emerging trends through automated data interpretation.

4. How to perform text analytics?

To perform text analytics, follow these steps:

1. Collect unstructured text data (emails, chats, reviews).
2. Preprocess text (cleaning, tokenization, lemmatization).
3. Apply NLP models for sentiment, topic, or entity analysis.
4. Visualize and interpret the insights for decision-making.

Unlock hidden insights from text data with AI-powered analytics.