NCA-GENL Question 83
Select 3You are tasked with analyzing a dataset containing millions of customer reviews to identify emerging trends in customer sentiment towards a product line. Which of the following techniques would be most appropriate for extracting insights from this large dataset?
- A
Using data visualization tools to identify patterns and trends in customer sentiment data
- B
Applying clustering algorithms to group reviews with similar sentiments
- C
Manually reading and categorizing the reviews for sentiment analysis
- D
Training a large language model (LLM) to perform sentiment classification on the dataset
- E
Using statistical sampling to reduce the dataset size before performing trend analysis
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets requires a combination of scalable and effective techniques. Data visualization tools help uncover patterns, clustering algorithms group similar data points, and large language models enable automated and scalable sentiment analysis. Manual review is infeasible due to the dataset's size, and statistical sampling can lead to incomplete or biased results, which is why it is not the most appropriate solution here.
- A. Correct.
Using data visualization tools is crucial to identifying patterns and trends in large datasets. Visualization can help uncover actionable insights quickly.
- B. Correct.
Clustering algorithms can group reviews with similar sentiments, which is an effective way to extract insights from a large dataset without manual intervention.
- C. Incorrect.
Manually reading and categorizing reviews is highly time-consuming and impractical for datasets containing millions of entries, making it an ineffective solution.
- D. Correct.
Training a large language model (LLM) to perform sentiment classification is a scalable and efficient approach to process and analyze large datasets.
- E. Incorrect.
While statistical sampling can reduce dataset size, it risks omitting important data points and may not provide a complete picture of trends. It is not the most appropriate technique in this scenario.