CFA Level II ExamQuantitative MethodsMedium
A data scientist is analyzing a large dataset of customer interactions to predict future purchasing behavior. The dataset contains both structured numerical data (e.g., purchase amount, frequency) and unstructured text data (e.g., customer reviews, chat transcripts). To leverage all available information effectively, the data scientist decides to employ a machine learning approach that can handle this mixed data type. Which of the following Big Data analytical methods is most directly applicable for processing the unstructured text data in this scenario?
- ANatural Language Processing (NLP)
- BTime-Series Forecasting
- CRegression Analysis
- DClustering Algorithms
Show answer & explanationAnswer & explanation
Correct answer: A. Natural Language Processing (NLP)
The scenario specifically mentions 'unstructured text data' like 'customer reviews' and 'chat transcripts'. Natural Language Processing (NLP) is the field of artificial intelligence that deals with the interaction between computers and human language, specifically how to program computers to process and analyze large amounts of natural language data.
Why the other options are wrong
- B. Time-series forecasting is used for predicting future values based on historical time-ordered data, and is not directly applicable to processing unstructured text data for insights.
- C. Regression analysis is used for modeling relationships between variables and predicting numerical outcomes, primarily with structured data, not directly for processing unstructured text.
- D. Clustering algorithms are used to group similar data points together. While they can be applied to text data (after vectorization), NLP is the foundational method for *processing* and extracting features from the raw text itself.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language, allowing for the analysis and processing of unstructured text data.
- Key for analyzing sentiment, extracting entities, summarizing text.
- Transforms unstructured text into structured features for ML models.
- Applications: chatbots, spam detection, language translation, text analytics.
Memory trick: Big Data analysis: Regress, Cluster, NLP, and Time.