How Does It Apply to Text Business Data Analysis?Įxercise 5.1 - Case Study Using Dataset A: Training SurveyĮxercise 5.2 - Case Study Using Dataset D: Job DescriptionsĮxercise 5.3 - Case Study Using Dataset C: Product ReviewsĪdditional Exercise 5.4 - Case Study Using Dataset B: Consumer ComplaintsĮxercise 6.1 - Case Study Using Dataset D: Resume and Job DescriptionĮxercise 6.2 - Case Study Using Dataset G: University CurriculumĮxercise 6.3 - Case Study Using Dataset C: Product ReviewsĪdditional Exercise 6.4 - Case Study Using Dataset B: Customer ComplaintsĮxercise 7.1 - Case Study Using Dataset C: Product Reviews - RubbermaidĮxercise 7.2 - Case Study Using Dataset C: Product Reviews-WindexĮxercise 7.3 - Case Study Using Dataset C: Product Reviews-Both BrandsĮxercise 8.1 - Case Study Using Dataset A: Training SurveyĮxercise 8.2 - Case Study Using Dataset B: Consumer ComplaintsĮxercise 8.3 - Case Study Using Dataset C: Product ReviewsĮxercise 8.4 - Case Study Using Dataset E: Large Text Files What are Some Examples of Text-Based Analytical Questions?Īdditional Case Study Using Dataset J: Remote Learning Student SurveyĬhapter 3 : Text Data Sources and FormatsĬustomer opinion data from commercial sitesĮxercise 4.1 - Case Study Using Dataset L: ResumesĮxercise 4.2 - Case Study Using Dataset D: Occupation DescriptionsĪdditional Exercise 4.3 - Case Study Using Dataset I: NAICS CodesĮxercise 4.4 - Case Study Using Dataset D: Occupation DescriptionsĪdditional Advanced Exercise 4.5 - Case Study Using Dataset E: Large Data FilesĪdditional Advanced Exercise 4.6 - Case Study Using Dataset F: The Federalist Papers What are the Characteristics of Well-framed Analytical Questions?Įxercise 1.1 - Case Study Using Dataset K: Titanic Disaster As you can see in the sample output, the description which has the keyword "Kitchen" is categorized to Restaurant based on the linkage between the words restaurant and kitchen.How Does Data Analysis Relate to Decision Making?
The values in the weightage column is based on how close the terms are related to each other. In reference to the sample output that I've given, Word Matched is picked up from the Restaurants data set and Word Linked is picked up from the Description column of the transaction dataset. TOPSY'S KITCHEN PETALUMA CA Restaurant Restaurant KITCHEN 98% USAA CO SPG CAXXXXXXXX COLORADO SPGSCO Other N/A N/A 0%īREAD PARTNERS #4 MIAMI FL Restaurant Restaurant BREAD 95% The final output should be of the format given below: DESCRIPTION Predicted_Category Word Matched Word Linked WeightageĬitibank Online Ref. I need to categorize every row in the transaction data set into a category called "Restaurant" or "Other" based on the relationship between the terms contained within the description and the terms that I already have in the Restaurant data set. I have another data set called Transaction which has text data describing about the transaction details. I have a set of terms (or keywords to be more precise) that belongs to a category called Restaurants in my Restaurant data set as shown below: RESTAURANTS