Your business has access to countless data sources, including feedback from your clients, customers, employees, and vendors. These open-ended responses can be any text comments, such as social media posts, customer reviews, survey responses, and more, but analyzing it properly is challenging.
Text analytics, also known as text mining or text analysis, is the process of extracting meaningful insights and patterns from unstructured text data. It involves various techniques from natural language processing (NLP), machine learning, and computational linguistics to analyze and understand the content of open end comments.
The main goals of text analytics are to derive actionable insights, discover trends, and extract useful information from large volumes of text responses.
Key Components of Text Analytics
The text analytics process starts with a data set that has open end responses which may or may not include closed end responses, also called quantitative data.
Text analytic solutions have the ability to work with data sets that are far too large to process manually, enabling your business to gain important research information that can drive your marketing strategies, customer service policies, budget allocation, product development, and countless other operations. Key components of text analytics include,
- Topic Analysis: Identifying topics or themes within the text data.
- Sentiment Analysis: Determining the sentiment (positive, negative, or neutral) expressed in the text.
- Clustering: Grouping similar texts together based on their content.
- Text Summarization: Generating a concise summary of the text data.
- Visualization: Visualizing the insights derived from text analytics through charts, graphs, word clouds, or other visual representations to facilitate interpretation.
How Is Text Analytics Used By Companies?
Text analytics is used by companies in various ways to extract valuable insights from large volumes of unstructured text data. Here are some key applications:
1. Customer Sentiment Analysis
- Purpose: Understand customer opinions and feelings about products or services.
- Application: Analyzing customer reviews, social media posts, and survey responses to gauge satisfaction and identify areas for improvement.
2. Industry Research
- Purpose: Identify market trends and consumer preferences.
- Application: Analyzing news articles, blogs, and online forums to understand industry dynamics and emerging trends.
3. Product Improvement
- Purpose: Enhance product features based on customer feedback.
- Application: Analyzing feedback from various channels to identify common issues and areas for improvement.
4. Customer Support
- Purpose: Improve customer support services.
- Application: Analyzing support tickets, chat logs, and email communications to identify common issues and streamline support processes.
5. Human Resources
- Purpose: Enhance employee experience and recruitment processes.
- Application: Analyzing employee feedback, performance reviews, and recruitment data to improve HR practices and employee satisfaction.
Capabilities of Text Analytics
Each text analytics tool has its own set of capabilities, but there are a number of features that you’ll commonly find in leading solutions on the market:
- Able to Analyze Both Structured & Unstructured Data
- Generates Clear & Descriptive Insights
- Processes Datasets of Any Size Quickly & Affordably
- Groups Data Together Logically
- Able to Drill Down to the Original Response
- Able to Trend Data
- Leverages the Latest AI Technologies
- Able to Adjust the Level of Generative AI by Project
- Produces Customizable Visualizations & Reports
- Offers Automatic Translation
- Enables Cross Tabs and Further Analysis
- Provides Data Scrubbing
- Has API Connectors
- Easily Imports & Exports Data
The Benefits of Text Analytics
Text analytics delivers many advantages to your organization. It’s a critical part of extracting value from data sets with open end responses that you’re otherwise unable to process.
- Works with open end comments in many types of media or language.
- Gives you insights to improve experiences for customers, employees, and other stakeholders.
- Gives you insights to help increase your company’s revenue.
- Reduces the time and effort needed to analyze unstructured data.
- Gives you the data you need to better control your costs.
- Helps you make more data-driven decisions.
- Enables you to act quickly on new opportunities.
What’s the Difference Between Text Mining and Text Analytics?
Though often used interchangeably, text mining and text analytics have distinct meanings and applications. Text mining is the process of discovering patterns and extracting useful information from large sets of unstructured text data.
It focuses on extracting information and knowledge from text using information extraction, categorization, clustering, association rule learning, and pattern recognition. The primary application of text mining is in data mining, where the goal is to uncover new patterns and insights.
On the other hand, text analytics encompasses a broader range of techniques used to analyze text data and extract meaningful insights. It includes the interpretative aspect of the results obtained from text mining, focusing on applying and interpreting these results to solve specific business problems.
Techniques used in text analytics include sentiment analysis, topic modeling, trend analysis, predictive analytics, and natural language processing (NLP). These techniques are employed in market research analysis, customer sentiment analysis, product review analysis, and other applications.
Find the Best Text Analytics Solution with Ascribe
If you are looking for a text analysis solution, check out CX Inspector with Theme Extractor and Generative AI. It’s our full-featured interactive software with generative AI that instantly analyzes open end responses, and lets you group ideas together, and explore data with sentiment, filters, crosstabs, and trend reports.