Text mining is an Artificial Intelligence (AI) technology that uses Natural Language Processing (NLP) to gain insights from unstructured text.
According to research, less than 1% of the data in the world is analyzed and processed. As businesses learn how to leverage data, they did not even know they had, they experience a paradigm shift in the way business is done. By using text mining and text analysis, a business can gain quantitative insights by tapping into text analytics.
The vast amount of data generated every day represents a tremendous opportunity for businesses across the world. It can be used to gain granular insights into customer feedback on a product or service.
This can be achieved by the flow of data from:
– Product reviews
– Social media analytics
– Customer surveys
All these sources of data need to be processed to be actionable. That is where the practice of text mining comes in.
How Text Mining Works
Text Mining v. Text Analytics
To establish how text mining works, a distinction between text analytics v. text mining needs to be made. Text mining can be distinguished by the fact that it works on qualitative insights while text analytics produces quantitative insights. For example, text mining can analyze customer feedback to determine if customers are dissatisfied with a product. Text analytics can gather deeper insights on customer behavior from unstructured text, such as identifying a pattern or trend of customer behavior. For example, text analytics can be used to interpret a decline in the popularity of a product over time.
Qualitative insights from text mining can be enhanced using NLP. For example, one of the common uses of NLP/text analytics is social media monitoring. Social media monitoring is done on a large sample of user-generated content to understand behavior trends, emotional sentiment, and awareness about a given topic. Once that data is processed new and valuable insight can be gleaned into the collective customer mindset from social media behavior.
Types of Text Mining
There are different types of text mining that can be used for analysis:
1. Word frequency
Used to find recurrent terms or concepts in a data set. Finding patterns of recurring words in the unstructured text is beneficial in analyzing customer reviews as well as feedback and social media conversations. For example, if the word “overpriced” is recurrent on customer reviews, it may be a sign that the business needs to adjust pricing.
A collocation is a sequence of words that are next to each other. Typically, collocations are either bigrams such as a pair of words (enterprise-wide, decision making) or trigrams, a combination of three words (go the distance, see you later). When text mining can identify collocations in a data set it improves granularity and delivers better results.
This is used to identify the context of a word. Since human language has so many ambiguities and nuances, concordance can recognize the exact meaning of a word in a particular context.
How NLP is Used in Text Mining
NLP is a subset of AI that deals with communication. It can be powerful when combined with text mining to read information and identify what is most important. What is impressive about NLP/text mining is the sheer volume of data that can be analyzed across millions of documents in a data set for meaning and patterns.
Text mining And NLP Advantages:
· Saves time and resources
· Higher efficiency and less margin of error than human analysis
· Managing information flow
· Gather insights into valuable data
Text mining is a powerful resource for businesses to gain insights into large data sets and evaluate opportunities that are going to drive the business forward.