A consumer electronics brand's marketing team wants to monitor thousands of daily social media posts that mention its products and automatically tag each post as positive, negative, or neutral in tone, so the team can react quickly if a wave of negative posts appears about a newly launched phone. Which AI capability should the marketing team use?
Select an answer to reveal the explanation.
Short Explanation
You already know the posts are about the new phone, so what you need is a read on the mood behind each one. Sentiment analysis classifies tone as positive, negative, or neutral, letting you catch a wave of complaints fast. Topic extraction, language detection, and entity spotting don't tell you how people feel.
Full Explanation
Sentiment analysis is the correct capability. It evaluates text and classifies the emotional tone expressed, typically as positive, negative, or neutral, which directly matches the marketing team's need to tag thousands of posts by tone so they can spot a spike in negative reaction quickly. Key phrase extraction is incorrect because it identifies the main topics or subjects discussed in a passage of text, such as 'battery life' or 'camera quality,' but it does not determine whether the sentiment expressed about those topics is favorable or unfavorable. Language detection is incorrect because it identifies which language a piece of text is written in, which would be useful if posts arrived in multiple languages but does nothing to classify emotional tone once the language is known. Named entity recognition is incorrect because it locates and categorizes specific entities such as product names, people, or organizations mentioned in text, which could tell the team a post mentions their new phone but not whether the mention is praise or complaint. Only sentiment analysis produces the positive, negative, or neutral tag the team needs to detect a wave of unhappy customers in near real time.