AI-900: Unlocking Insights: Sentiment Analysis in Natural Language Processing

Explore the power of sentiment analysis in natural language processing. Discover how this technology enables businesses to analyze customer reviews, gauge sentiment, and derive valuable insights. Learn how sentiment analysis can enhance decision-making, improve customer experiences, and drive business growth.

Question

You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is. This is an example of which type of natural language processing workload?

A. language detection
B. sentiment analysis
C. key phrase extraction
D. entity recognition

Answer

B. sentiment analysis

Explanation

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

The correct answer is B. sentiment analysis.

Sentiment analysis is a type of natural language processing workload that can determine how positive or negative a text is. Sentiment analysis can use advanced algorithms to assign a polarity score or a sentiment label to a text, such as happy, sad, angry, or neutral.

You are developing a natural language processing solution in Azure that will analyze customer reviews and determine how positive or negative each review is. This is an example of sentiment analysis, as it involves evaluating the tone or emotion of the customer reviews. Sentiment analysis can be useful for applications such as customer feedback, social media analysis, or product review.

The other three options are not types of natural language processing workloads that can determine how positive or negative a text is, but they can perform other tasks related to text analysis:

• Language detection is a type of natural language processing workload that can identify the language of a text, such as English, Spanish, or Chinese. Language detection can use advanced algorithms to compare the text with a set of known languages and return the most likely language or a list of possible languages.
• Key phrase extraction is a type of natural language processing workload that can extract the most important or relevant words or phrases from a text, such as keywords, topics, or themes. Key phrase extraction can use advanced algorithms to analyze the frequency, position, or context of the words or phrases and return a list of key phrases.
• Entity recognition is a type of natural language processing workload that can identify and label the names of specific things or concepts in a text, such as people, places, organizations, dates, or products. Entity recognition can use advanced algorithms to analyze the syntax, semantics, or morphology of the text and return a list of entities and their types.

References

Microsoft Docs > Azure > Architecture > Data Architecture Guide > Choose a natural language processing technology in Azure

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