Forums for the Business Analyst

 
  Modern Analyst Forums  Business and Sy...  Business Proces...  Overview of Text Mining
Previous Previous
 
Next Next
New Post 3/27/2018 11:43 PM
User is offline Rahul2015
6 posts
10th Level Poster


Overview of Text Mining 
Modified By Chris Adams  on 3/28/2018 11:10:54 AM)

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.

 
New Post 5/8/2018 9:17 PM
User is offline maverick 01
2 posts
No Ranking


Re: Overview of Text Mining 

It's an analytical field which derives high-quality information from text

 
Previous Previous
 
Next Next
  Modern Analyst Forums  Business and Sy...  Business Proces...  Overview of Text Mining

Community Blog - Latest Posts

Pjbussol
Pjbussol
  TRUST IN CONFLICT - THE HEALTHY WAY TO PICK A FIGHT!     Perched on top of the Royal Liver Building in Liverpool, the city I grew up in, is a very special couple; married for over 100 years and standing 18ft tall the legendary Liver Birds, Bella and Bertie have unflinchingly protected the city and her people since 1911. Bel...
2 Responses
Arash
Arash
Let me start by saying that a business rule is far more granular than a business decision, and that the two should never be confused. Today, it is an unfortunate fact that some vendors would have potential clients believe that there is synergy between business rules and business decisions. Perhaps this is because in an effort to differentiate one b...
0 Responses
LN Mishra, CBAP
LN Mishra, CBAP
Traceability is one of the lesser understood aspects of business analysis. It is indeed quite hard to maintain good traceability unless automated. This is why BABoK® warns us being theoretical about traceability. In this article, I would like to explain traceability concepts with help of an example. BABoK® definition of traceability: ...
4 Responses




Latest Articles

Business jargon….in a nutshell
Jul 15, 2018
2 Comments
With this article, I’ve done the heavy lifting for you, by mentioning some of these jargon-based pearls of wisdom here. You ...
Featured Digital Library Resources 
Copyright 2006-2018 by Modern Analyst Media LLC