AWS Certified Machine Learning – SpecialtyExploratory Data AnalysisEasy
A data science team is analyzing customer feedback text data to understand sentiment. They have collected over 100,000 comments. Before building a sentiment analysis model, they need to perform Exploratory Data Analysis (EDA). Which visualization technique would be most effective for quickly identifying the most frequently occurring positive and negative keywords in the entire corpus?
- AHeatmap, to visualize the co-occurrence matrix of keywords across documents.
- BScatter plot, to show the relationship between keyword frequency and sentiment score.
- CWord cloud, to visually represent keyword frequency with larger words indicating higher frequency.
- DBox plot, to display the distribution of sentiment scores for different keyword categories.
Show answer & explanationAnswer & explanation
Correct answer: C. Word cloud, to visually represent keyword frequency with larger words indicating higher frequency.
A word cloud is specifically designed to visually represent the frequency of words in a text corpus, where the size of each word directly corresponds to its frequency, making it ideal for quickly identifying common keywords.
Why the other options are wrong
- A. Incorrect. A heatmap of co-occurrence is useful for understanding relationships between keywords, but not for simply identifying the most frequent ones.
- B. Incorrect. A scatter plot shows relationships between two numerical variables, not directly keyword frequency in a corpus.
- D. Incorrect. A box plot is used to display the distribution of numerical data across different categories, not for visualizing keyword frequencies.
Word Cloud
A visual representation of text data where the size of each word indicates its frequency or importance within a given text corpus.
- Quickly highlights most frequent terms.
- Useful for text data EDA.
- Size and color can be used to convey additional information.
Memory trick: Words form clouds, showing what's most loud.