Text analytics vs. text mining can be distinguished by the fact that text mining works on qualitative insights while text analytics produces quantitative insights. For example, text mining can analyze surveys and reviews to see if customers are happy with a product. Text analytics can yield deeper insights on customer behavior from unstructured text, such as identifying a pattern or trend of customer behavior. Text analysis is an evolving technique that is revolutionizing the global economy. Part of this evolution has been the development of an array of techniques to derive meaning from text.
Text Analytics And Text Mining Use Different Approaches
While text analytics and text mining are often used interchangeably, they differ in that text analytics delivers quantitative insights and text mining delivers qualitative insights.
Text Analytics Approach
– Quantitative insights: rely on statistical modeling for “out of the box” solutions to any text analysis problem.
Text Mining Approach
– Qualitative insights: use linguistic rules to deliver complex outputs with more precision.
Text Analytics vs. Text Mining Examples
Texting Mining Used For Sentiment Analysis
Text mining is an older technique than text analysis. It has found an application in sentiment analysis or opinion mining on social media. Social media is a perfect breeding ground for text mining with:
– Facebook posts
– Instagram posts
– LinkedIn posts
Sentiment analysis uses Machine Learning and NLP to automatically analyze text for the sentiment of the writer. A single tweet can have a positive, negative, or neutral connotation for a brand based on the analysis of the sentiment of the writer.
– For example, the more information a business has about customers at its disposal, the more it can determine whether customers are happy with a product.
The unstructured data of social media posts presents an opportunity for businesses to become more responsive to customers and sustain long-term growth.
Text Analytics Used For Linguistic Approaches
Text analytics uses a linguistic approach for extracting value from unstructured textual data. Text analytics is more complex than text mining requiring sophisticated taxonomies or other structured lists as guidelines.
Once those guidelines are in place, text analytics works on large collections of unstructured data to discover new insights. Whereas text mining is better used to solve a particular problem, text analytics looks at the big picture of a problem.
– For example, a large data set of tweets are analyzed to predict a trend in customer behavior.
Actions made on data rather than guesswork enables informed decision-making that revolutionizes the course of business operations.
When it comes to leveraging the untapped value of unstructured data, text analytics and text mining are complementary techniques with different approaches. Text mining uses statistics while text analytics uses linguistics to wrangle unstructured data. Each can be practiced independently yet the most effective solutions combine their strengths.
Businesses can benefit from the balance of precision of linguistically based text analytics and the powerful recall of statistical text mining. With such a combination, great progress is being made on the future of text analytics in the world. A variety of textual analysis challenges can be met with text mining and text analytics leaving room for innovation.