Text Analytics DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 6)

Questions 26–29

1. 26foundation · easy

   Which type of data is text analytics most commonly applied to?

   • AUnstructured text such as emails, reviews, and social media posts
   • BSemi-structured data like JSON logs
   • CStructured data stored in relational tables
   • DNumerical data from sensors and IoT devices
   Reveal Solution Discussion Select an answer first
2. 27application · easy

   A legal firm needs to extract names of parties, judges, and court locations from thousands of legal documents to build a searchable database. Which text analytics technique should they use?

   • ANamed Entity Recognition (NER)
   • BSentiment analysis
   • CTF-IDF vectorization
   • DTopic modeling with LDA
   Reveal Solution Discussion Select an answer first
4. 28expert · hard

   A research team is using LDA to analyze a corpus of scientific abstracts. They notice that the topics are dominated by common academic words like 'study', 'research', and 'result', which appear across all documents. These words obscure the distinct themes. Which preprocessing change would most effectively improve topic distinctiveness?

   • AIncrease the number of topics in the LDA model
   • BAdd domain-specific stop words like 'study', 'research', and 'result' to the stop-word list
   • CUse TF-IDF instead of raw counts for the LDA input
   • DApply stemming to reduce words to their roots
   Reveal Solution Discussion Select an answer first
5. 29foundation · easy

   What is the primary task of Named Entity Recognition (NER)?

   • ATo identify and classify named entities such as people, organizations, and locations in text
   • BTo translate text from one language to another
   • CTo determine the overall sentiment of a document
   • DTo group documents into clusters based on similarity
   Reveal Solution Discussion Select an answer first