Tag: Keyword Extraction

AI, AI Governance, AI Strategy, AI-901, Microsoft Certification

Describe common Text Analysis techniques, including Keyword Extraction, Entity Detection, Sentiment Analysis, and Summarization (AI-901 Exam Prep)

 
This post is a part of the AI-901: Microsoft Azure AI Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Identify AI concepts and capabilities (40–45%)
   --> Identify AI workloads
      --> Describe common Text Analysis techniques, including Keyword Extraction, Entity Detection, Sentiment Analysis, and Summarization

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Text analysis is one of the most common and important AI workloads covered in the AI-901 certification exam. Microsoft expects candidates to understand how AI systems analyze and interpret written language using Natural Language Processing (NLP) techniques.

This topic falls under the “Identify AI workloads” section of the AI-901 exam objectives.

What Is Text Analysis?

Text analysis is an AI workload that uses Natural Language Processing (NLP) to analyze, interpret, and extract meaning from written text.

Text analysis helps organizations process large amounts of unstructured textual data automatically.

Common Sources of Text Data

Organizations analyze text from many sources, including:

  • Emails
  • Customer reviews
  • Social media posts
  • Chat messages
  • Support tickets
  • Surveys
  • Documents
  • Articles

What Is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a branch of AI focused on helping computers understand and work with human language.

NLP combines:

  • Machine learning
  • Linguistics
  • Statistical analysis
  • Deep learning

NLP enables systems to interpret meaning, emotion, intent, and context within text.

Common Text Analysis Techniques

For the AI-901 exam, important text analysis techniques include:

  • Keyword extraction
  • Entity detection
  • Sentiment analysis
  • Summarization

Additional related techniques include:

  • Language detection
  • Translation
  • Text classification

Keyword Extraction

Keyword extraction identifies the most important words or phrases within text.

The goal is to determine the primary topics or themes.

How Keyword Extraction Works

AI systems analyze text and identify terms that appear most significant based on:

  • Frequency
  • Relevance
  • Context
  • Relationships to other words

Keyword Extraction Examples

Input Text

“The customer was very satisfied with the fast delivery and excellent product quality.”

Extracted Keywords

  • customer
  • fast delivery
  • product quality

Common Use Cases for Keyword Extraction

Search Optimization

Improve document indexing and search engines.

Document Categorization

Identify major document topics automatically.

Customer Feedback Analysis

Detect common issues or themes.

Content Tagging

Automatically assign tags to articles or documents.

Entity Detection

Entity detection identifies important entities mentioned within text.

This technique is often called Named Entity Recognition (NER).

Common Entity Types

AI systems may identify:

  • People
  • Organizations
  • Locations
  • Dates
  • Phone numbers
  • Email addresses
  • Products
  • Currency amounts

Entity Detection Example

Input Text

“Microsoft announced a conference in Seattle on June 15.”

Detected Entities

  • Microsoft → Organization
  • Seattle → Location
  • June 15 → Date

Common Use Cases for Entity Detection

Document Processing

Extract important business information from contracts or forms.

Compliance Monitoring

Identify sensitive information.

Customer Relationship Management

Track companies, customers, or products mentioned in communications.

Search and Analytics

Improve document filtering and organization.

Sentiment Analysis

Sentiment analysis identifies emotional tone or opinion within text.

It determines whether text expresses:

  • Positive sentiment
  • Negative sentiment
  • Neutral sentiment

How Sentiment Analysis Works

AI models analyze words, phrases, and context to estimate emotional tone.

Example Positive Words

  • Excellent
  • Great
  • Amazing

Example Negative Words

  • Poor
  • Terrible
  • Frustrating

Context is important because words can have different meanings depending on usage.

Sentiment Analysis Example

Input Text

“The product quality was excellent, but shipping was slow.”

Possible Sentiment Results

  • Product quality → Positive
  • Shipping experience → Negative

Some systems provide:

  • Overall sentiment
  • Sentence-level sentiment
  • Confidence scores

Common Use Cases for Sentiment Analysis

Customer Feedback Monitoring

Analyze reviews and surveys.

Brand Monitoring

Track public opinion on social media.

Customer Service Improvement

Identify dissatisfied customers.

Market Research

Understand consumer opinions.

Summarization

Summarization creates shorter versions of longer text while preserving key information.

AI summarization helps users quickly understand large amounts of information.

Types of Summarization

Extractive Summarization

Extractive summarization selects important sentences directly from the original text.

Abstractive Summarization

Abstractive summarization generates new sentences that summarize the meaning of the text.

This approach is more similar to how humans summarize information.

Summarization Example

Original Text

“The company reported increased sales this quarter due to strong online demand and improved supply chain performance.”

Summary

“The company experienced increased sales driven by online demand.”

Common Use Cases for Summarization

Meeting Summaries

Condense meeting transcripts.

News Summaries

Provide quick article overviews.

Customer Support

Summarize long support conversations.

Research Assistance

Condense lengthy documents or reports.

Language Detection

Language detection identifies the language used in text.

Example

An AI system determines whether text is:

  • English
  • Spanish
  • French
  • German

Common Use Cases

  • Multilingual applications
  • Translation routing
  • International customer support

Text Classification

Text classification assigns categories or labels to text.

Examples

  • Spam detection
  • Topic categorization
  • Support ticket routing

Real-World Examples

Scenario 1: Customer Review Analysis

Goal

Understand customer opinions.

Techniques Used

  • Sentiment analysis
  • Keyword extraction

Scenario 2: Legal Contract Processing

Goal

Identify important contract information.

Techniques Used

  • Entity detection
  • Summarization

Scenario 3: News Aggregation Platform

Goal

Provide short summaries of articles.

Techniques Used

  • Summarization
  • Keyword extraction

Scenario 4: Customer Support Ticket System

Goal

Automatically categorize and prioritize tickets.

Techniques Used

  • Text classification
  • Sentiment analysis

Azure AI Language Services

Azure AI Language Services provide prebuilt NLP capabilities such as:

  • Sentiment analysis
  • Entity recognition
  • Summarization
  • Language detection
  • Key phrase extraction

These services help developers add text analysis features without building models from scratch.

Structured vs. Unstructured Text Data

Text analysis commonly processes unstructured data.

Structured DataUnstructured Data
DatabasesEmails
TablesDocuments
SpreadsheetsSocial media posts
Defined fieldsReviews

AI systems help convert unstructured text into usable structured information.

Responsible AI Considerations

Organizations using text analysis should consider:

  • Privacy
  • Bias
  • Transparency
  • Security
  • Accuracy
  • Responsible handling of personal data

Text analysis systems may process sensitive information and should be designed carefully.

Important AI-901 Exam Tips

For the exam, remember these key points:

  • Keyword extraction identifies important terms or phrases.
  • Entity detection identifies items such as people, places, organizations, and dates.
  • Sentiment analysis determines emotional tone.
  • Summarization creates shorter versions of text.
  • NLP enables computers to process human language.
  • OCR extracts text from images but is different from text analysis.
  • Summarization may be extractive or abstractive.
  • Text classification assigns categories to text.

Quick Knowledge Check

Question 1

Which text analysis technique identifies emotional tone?

Answer

Sentiment analysis.

Question 2

What does Named Entity Recognition (NER) identify?

Answer

Entities such as people, organizations, locations, and dates.

Question 3

What is the purpose of keyword extraction?

Answer

To identify important words or phrases in text.

Question 4

What does summarization do?

Answer

Creates shorter versions of longer text while preserving key information.

Practice Exam Questions

Question 1

Which text analysis technique identifies the emotional tone of written text?

A. OCR
B. Sentiment analysis
C. Object detection
D. Regression

Correct Answer

B. Sentiment analysis

Explanation

Sentiment analysis determines whether text expresses positive, negative, or neutral emotions or opinions.

Why the Other Answers Are Incorrect

A. OCR

OCR extracts text from images or scanned documents.

C. Object detection

Object detection identifies objects within images.

D. Regression

Regression predicts numeric values.

Question 2

A company wants to automatically identify important phrases from customer feedback forms.

Which text analysis technique is MOST appropriate?

A. Speech synthesis
B. Keyword extraction
C. Facial recognition
D. Image classification

Correct Answer

B. Keyword extraction

Explanation

Keyword extraction identifies the most important words or phrases within text.

Why the Other Answers Are Incorrect

A. Speech synthesis

Speech synthesis converts text into spoken audio.

C. Facial recognition

Facial recognition analyzes faces in images.

D. Image classification

Image classification categorizes images.

Question 3

What is the PRIMARY purpose of Named Entity Recognition (NER)?

A. Predicting future sales
B. Identifying important entities such as people, organizations, and locations in text
C. Translating languages automatically
D. Detecting objects in images

Correct Answer

B. Identifying important entities such as people, organizations, and locations in text

Explanation

NER extracts structured information from text by identifying entities like names, places, dates, and organizations.

Why the Other Answers Are Incorrect

A. Predicting future sales

This is typically a regression task.

C. Translating languages automatically

Translation is a separate NLP capability.

D. Detecting objects in images

This is a computer vision task.

Question 4

Which AI capability creates a shorter version of a document while preserving key information?

A. OCR
B. Summarization
C. Clustering
D. Object detection

Correct Answer

B. Summarization

Explanation

Summarization condenses long text into shorter, meaningful summaries.

Why the Other Answers Are Incorrect

A. OCR

OCR extracts text from images.

C. Clustering

Clustering groups similar data.

D. Object detection

Object detection identifies items within images.

Question 5

A business analyzes product reviews to determine whether customers are satisfied or dissatisfied.

Which AI technique is being used?

A. Sentiment analysis
B. Recommendation system
C. OCR
D. Regression

Correct Answer

A. Sentiment analysis

Explanation

Sentiment analysis evaluates emotional tone and opinions expressed in text.

Why the Other Answers Are Incorrect

B. Recommendation system

Recommendation systems suggest products or content.

C. OCR

OCR extracts text from images.

D. Regression

Regression predicts numeric outcomes.

Question 6

Which statement BEST describes keyword extraction?

A. It converts speech into text
B. It identifies important words or phrases in text
C. It translates text between languages
D. It predicts future trends

Correct Answer

B. It identifies important words or phrases in text

Explanation

Keyword extraction helps determine the main topics or themes within text documents.

Why the Other Answers Are Incorrect

A. It converts speech into text

This is speech recognition.

C. It translates text between languages

This is machine translation.

D. It predicts future trends

This is unrelated to keyword extraction.

Question 7

Which text analysis technique would MOST likely identify “Microsoft” as an organization and “Seattle” as a location?

A. Entity detection
B. Sentiment analysis
C. Speech recognition
D. Image segmentation

Correct Answer

A. Entity detection

Explanation

Entity detection (NER) identifies named entities such as organizations, locations, dates, and people within text.

Why the Other Answers Are Incorrect

B. Sentiment analysis

Sentiment analysis evaluates emotional tone.

C. Speech recognition

Speech recognition processes audio.

D. Image segmentation

Image segmentation is a computer vision task.

Question 8

What is the difference between extractive and abstractive summarization?

A. Extractive summarization uses images, while abstractive summarization uses text
B. Extractive summarization selects sentences from the original text, while abstractive summarization generates new summary wording
C. Extractive summarization only works with speech
D. There is no difference

Correct Answer

B. Extractive summarization selects sentences from the original text, while abstractive summarization generates new summary wording

Explanation

Extractive summarization pulls existing sentences directly from text, while abstractive summarization creates newly generated summaries.

Why the Other Answers Are Incorrect

A. Extractive summarization uses images, while abstractive summarization uses text

Both methods work with text.

C. Extractive summarization only works with speech

Summarization is generally text-based.

D. There is no difference

The two methods are different approaches.

Question 9

Which AI workload category includes keyword extraction, sentiment analysis, and summarization?

A. Computer vision
B. Text analysis
C. Robotics
D. Regression analysis

Correct Answer

B. Text analysis

Explanation

These techniques are part of Natural Language Processing (NLP) and text analysis workloads.

Why the Other Answers Are Incorrect

A. Computer vision

Computer vision focuses on images and video.

C. Robotics

Robotics involves physical machines and automation.

D. Regression analysis

Regression predicts numeric values.

Question 10

A company wants to process thousands of support tickets and automatically identify the most common customer complaints.

Which AI technique would be MOST useful?

A. Object detection
B. Keyword extraction
C. Facial recognition
D. Speech synthesis

Correct Answer

B. Keyword extraction

Explanation

Keyword extraction identifies recurring important phrases and themes within large collections of text.

Why the Other Answers Are Incorrect

A. Object detection

Object detection analyzes images.

C. Facial recognition

Facial recognition identifies people in images or video.

D. Speech synthesis

Speech synthesis converts text into audio.

Final Thoughts

Text analysis is a foundational AI workload and an important topic for the AI-901 certification exam. Microsoft expects candidates to understand common NLP techniques and recognize real-world scenarios where text analysis provides value.

These capabilities help organizations transform large volumes of unstructured text into actionable insights using Azure AI technologies.

Go to the AI-901 Exam Prep Hub main page