NCA-GENL Question 81
Select 3A company wants to analyze customer reviews for a product to identify common sentiments and trends. They plan to use data mining and data visualization techniques. Which of the following steps are essential in this process?
- A
Preprocessing the data to clean and structure textual information from the reviews
- B
Running a sentiment analysis model to classify reviews as positive, negative, or neutral
- C
Directly visualizing the raw text data without any transformation
- D
Using data visualization techniques such as word clouds or bar charts to display key trends
- E
Ignoring outliers and anomalies in the data as they do not affect overall trends
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets using data mining and data visualization involves preprocessing the data to ensure accuracy, applying analytical models like sentiment analysis to extract meaningful information, and leveraging visualization techniques to display trends and patterns effectively. Skipping essential steps like preprocessing or ignoring outliers can compromise the quality of insights.
- A. Correct.
Preprocessing the data is a critical step to clean, organize, and prepare raw textual data for analysis. Without this step, the data may contain noise that can hinder accurate insights.
- B. Correct.
Running a sentiment analysis model is essential to classify and extract meaningful insights from customer reviews. This step helps in understanding the polarity of opinions.
- C. Incorrect.
Directly visualizing raw text data is ineffective because unprocessed text can be unstructured and difficult to interpret. Preprocessing and analysis are needed first.
- D. Correct.
Data visualization techniques like word clouds or bar charts help in representing trends and patterns effectively, making it easier to derive actionable insights.
- E. Incorrect.
Ignoring outliers and anomalies can lead to skewed results, as these data points might represent important, unique insights or errors that need attention.