Hello everyone, I have a question about Text Mining. We have a data that includes whole job applications with candidate information and we have also data includes whole job postings with employer information and these datas are historical. These data sets will be a live data in the future but we currently proceed over a specific reporting date(snapshot). For Job Postings we have two information; Employer ID Job Description We created a single column that is called Job Description by pulling the following values; Gender Location Military Status Educational Status Social Status (Disabled, Veteran etc.), Marital Status Driver's License Foreign Language For Job Applications we have two information; Employee ID Candidate Information We created a single column that is called Candidate Information by pulling the following values; Gender Location Military Status Educational Status Social Status (Disabled, Veteran etc.), Marital Status Driver's License Foreign Language As you know, the nodes I can use for Text Miner on Enterprise Miner are as follows; Import, Parsing, Filter, Topic, Cluster, Profile and Rule Builder The targeted operations with Text Mining are as follows; Our goal is to find out how well the Candidate Information and the Job Description table match. Assigning a score according to the match rate. We want to protect the top 30 candidates with the best score by avoiding multiplication. As a result we want to get an output similar to the table below. KEY RANK Employer_ID JOB_DESCRIPTION (TEXT) Employee_ID CANDIDATE_INFORMATION (TEXT) MATCH SCORE 1 1 1 A B c d e 10 A B C D E F 100 2 2 1 A B c d e 20 A B C D E 100 3 3 1 A B c d e 30 A B C 60 4 4 1 A B c d e 70 C UK 20 : : 1 : : : : 30 30 1 A B c d e 40 A 20 31 1 2 TR uK usA ITA 90 TR UK USA ITA 100 32 2 2 TR uK usA ITA 50 TR UK 50 33 3 2 TR uK usA ITA 60 TR USA 50 34 4 2 TR uK usA ITA 80 TR 25 35 : 2 : : : : : 30 2 TR uK usA ITA 70 C UK 25 It would be nice if we could get guidance on this case with the Text Miner perspective.
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