Learn how to use Text Analytics to analyze the sentiment of a document and extract the main talking points. Use key phrase extraction and sentiment analysis features of Text Analytics to quickly identify the main points in a collection of documents.
Table of Contents
Question
You are planning on creating a solution that will analyze the sentiment of a document and will extract the main talking points. Which feature(s) of Text Analytics should you take into account? Select all options that apply.
A. Entity recognition
B. Key phrase extraction
C. Sentiment analysis
Answer
B. Key phrase extraction
C. Sentiment analysis
Explanation
B. Key phrase extraction: You can use key phrase extraction to quickly identify the main concepts or talking points in a document.
C. Sentiment analysis: You can use sentiment analysis to evaluate the text of a document and get sentiment scores (positive, neutral, negative) for each sentence.
To analyze the sentiment of a document and extract the main talking points, you should take into account key phrase extraction and sentiment analysis features of Text Analytics. Key phrase extraction evaluates unstructured text and returns a list of key phrases, which is useful if you need to quickly identify the main points in a collection of documents. Sentiment analysis provides a way for detecting the sentiment labels (such as “negative”, “neutral” and “positive”) and confidence scores at the sentence and document-level. Entity recognition identifies different entities in text and categorizes them into pre-defined classes or types. However, entity recognition is not directly related to the task of analyzing sentiment and extracting main talking points. Therefore, option A is not applicable in this scenario.
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