An introduction to text analytics

Text analytics solutions can now discover information and provide value faster than ever before.

What is text analytics?

Text analytics, also known as text mining, is the process of deriving high-quality information from written communication. Text analytics applications and systems automate the otherwise time-consuming task of manually reading unstructured text feedback to uncover insights. Text analytics software can extract meaning from context with processes such as identifying themes, providing sentiment analysis, and showing relationships between words.

Decades ago, text analytics involved simple tasks like calculating word frequencies. Over the last few years, artificial intelligence technologies like natural language understanding (NLU) and machine learning, and techniques like deep learning have dramatically improved the effectiveness of text analytics.

What problems does text analytics solve?

Text analytics solutions can solve for many different types of challenges. Some target very specific problems, like examining legal documents for compliance, detecting fraud or spam in emails, or monitoring social media. In other cases, generic text analytics features may be thrown into a larger data analytics suite or customer experience (CX) solution to provide a basic level of capability for all users.

A growing area of focus for text analytics is in analyzing unstructured text from customer feedback. CX teams, customer support centers, and market researchers find themselves buried under an avalanche of communications from people who want their voices heard. However, Voice of the Customer (VoC) initiatives can’t be effective unless analysts can automate the processing of customer feedback. Teams need to extract insights quickly enough to take informed actions.

Find, triage, and solve digital problems with text analytics

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How do I get started with text analytics?

With so many different possibilities for implementing text analytics in your organization, you’ll want to narrow down your use case before evaluating your options.

Choose a source of text to analyze. Rich, unstructured customer feedback such as survey verbatims, product reviews, and support tickets often lay untapped within your organization. Choose data that you would read yourself if you had the time and resources to do so.

Decide how much to analyze and how often. Knowing the volume of data you’ll want to analyze, and how frequently you’ll want the analysis, will help you choose an appropriate text analytics solution. Analyzing several hundred verbatims from an annual customer satisfaction survey may require a different approach than processing thousands of incoming complaints to look for reported defects every two weeks after product updates.

Identify your resources. Do you have a centralized team of analysts and data scientists at your disposal? Or are you a team of one? Knowing what technical expertise and in-house tools you can leverage will influence which application you’ll need to solve your specific business problem.

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    What sets Luminoso apart from other text analytics solutions?

    Many text analytics solutions rely on extensive manual setup work by a team of subject matter experts (SMEs). SMEs must train systems to recognize specialized terminology, customer slang, and company-specific words such as brands or products. SMEs must then periodically retrain systems to adjust for new products, market trends, and the shifting language of their customers.

    Luminoso is different. Our text analytics application is powered by proprietary QuickLearn® technology, which incorporates the latest research in AI and NLU. Leveraging a knowledge base of more than 34 million relationships between words in multiple languages, QuickLearn provides a common-sense understanding of the world and how people use language – meaning our applications require no training, setup, or libraries of specialized words assembled over time by experts. Luminoso immediately builds a nuanced understanding of customer feedback datasets, without any machine learning training on industry-specific language.

    Get started with text analytics today.

    Knowing where to begin with text analytics can be difficult. Grab the Gartner Market Guide for Text Analytics to help you tackle your specific business challenge with the right text analytics solution.

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