LDA requires labeled training data where each document is already categorized into predefined topics. ✅ False Explanation:LDA is an unsupervised learning method, meaning it does not require labeled data. Instead of assigning documents to predefined categories, it discovers hidden topics in the text by identifying word co-occurrence patterns.

Which of the following developed the psychosexual stages of development?

A company specializing in industrial equipment sales wants to analyze thousands of customer feedback messages to identify key complaint themes. They do not have predefined complaint categories but want to uncover patterns in the data. Which technique should they use? A) Sentiment analysisB) Named entity recognitionC) Latent Dirichlet Allocation (LDA)D) Supervised machine learning Answer: C) Latent Dirichlet Allocation (LDA) Explanation: LDA is a topic modeling technique that helps businesses identify hidden themes in large collections of unstructured text, such as customer complaints. Since the company does not have predefined categories, LDA is the best choice. Sentiment analysis (option A) determines emotional tone but does not uncover themes. Named entity recognition (option B) extracts specific entities (e.g., company names), not complaint topics. Supervised machine learning (option D) requires labeled data, which the company does not have.

Clients have autonomy within the counseling relationship. 

Reflection of feeling, active listening, and encouragers are all counseling skills. 

School Counselors may have additional duties such as test coordination, parent consultation, and referrals. 

An e-mail provider is using machine learning to classify incoming emails as “urgent” or “not urgent” based on a dataset of previously labeled (or categorized) emails. Which of the following best describes this approach?

Which of the following correctly compares topic modeling and text classification?

CBT was established by Ellis.

A video game company wants to analyze customer reviews to identify emerging concerns about a new product. They are unsure what the key themes in the reviews are and want to uncover them. Which text mining technique should they use?