Text mining is an analytical field which derives high-quality information from text. Text mining is widely used in the industry when data is unstructured. Derived information can be provided in the form of numbers (indices), categories or clusters, a summary of the text. In this blog, we will focus on applications of text mining, workflow, an example.

Text Mining Applications

1. Analyze open-ended survey comments- Analysis of open-ended comments is most common in the current market. When a particular survey is conducted, there are options for the customers to provide feedback to the company using open ends rather than constraining their opinions into the particular dimension of scaling. Sometimes, these open ends are more than 5000 words and hence, human mind can’t gather and extract information. The best possible solution is to use text mining algorithms.

2. Analyze customer insurance/warranty claims, feedback forms, etc.- In insurance domain, warranty claims information are usually open-ended. For example, when a motor claim is filed, insured specifies the reason of accident in textual comments and you can imagine how difficult and erroneous it can be to process the huge number of the motor claim by a company in a month.

3. Analyze sentiment of users against a particular product/campaign/reviews using social media data- Every company are worried about their brand, customer satisfaction, and customer preference. It takes just seconds for a customer to go on the internet and spread bad words about a company. Social media analytics uses text mining to compute sentiment of the customer. It’s easy to identify core topic discussed among customers every day on social media using text mining.

4. Automatic processing of emails/images/messages etc.- Text mining algorithms are used for automatic classification of texts. In outlook, a user categorizes the emails into various folders/spam. Similarly, on a larger scale using text mining algorithms, key topics can be identified and the emails can be automatically forwarded to the desired department

5. Identify competitors performance- In business intelligence sector, identifying competitors performance, capabilities, products offered, identifying their target business line can be automatically processed using the combination of web crawling and text mining.

6. Automatic document search- In recent months, researchers have focused on text mining to identify reference documents for their research. For example-You are a researcher and would want to figure out the summary of a chapter in a document. There are two ways to go through; one is read the entire chapter or use text mining algorithms.