Content Type:

Glossary

Text Analytics Definition

The process of deriving high-quality information from text using statistical pattern learning. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling. The term is roughly synonymous with text mining and data mining.

Text analytics is the process of analyzing and deriving insights from large volumes of text-based data, such as customer feedback, survey responses, social media posts and online reviews. Using natural language processing (NLP) and computational techniques, text analytics transforms unstructured textual information into structured data, enabling researchers to identify patterns, sentiments and trends for actionable insights.

How does text analytics work in market research?

Text analytics involves several key processes:

• Data collection: Gathering textual data from various sources, such as surveys, social media and customer reviews.
• Text preprocessing: Cleaning and preparing the data, including tokenization, removing stop words and stemming.
• Analysis techniques: Applying NLP and machine learning methods to categorize text, extract key entities, detect sentiment and summarize content.
• Insights generation: Visualizing results through dashboards or reports to identify patterns, trends and actionable insights.

Common text analytics tasks include:

• Sentiment analysis: Determining the tone (positive, negative, neutral) of the text.
• Text categorization: Classifying text into predefined categories.
• Topic modeling: Identifying themes or topics within the text.
• Keyword extraction: Highlighting the most frequently mentioned terms or concepts.

Why is text analytics important?

Text analytics is vital for leveraging the wealth of unstructured data available in today’s digital landscape. By converting text into meaningful insights, businesses can better understand customer sentiments, track brand perception and identify emerging trends, leading to more informed and timely decisions. Text analytics unlocks valuable insights from unstructured text data. It provides a deeper understanding of customer opinions and behaviors and helps track brand reputation and emerging industry trends in real time.

Who relies on text analytics?

A wide range of professionals and teams benefit from text analytics, including market researchers, to extract insights from survey responses and customer reviews; brand managers, to monitor brand perception and identify potential issues; customer experience teams, to understand customer feedback and improve service; social media analysts, to analyze conversations and sentiments across platforms; and businesses, to gain competitive insights and improve decision-making.