A subscription business collects thousands of free-text customer reviews each week and wants an automated way to classify each review as expressing a positive, negative, or neutral opinion, so the support team can prioritise unhappy customers first. Which text analysis technique fits this requirement?
- AKeyword extraction, which surfaces the main talking points and terms mentioned across the review text
- BEntity detection, which locates the named people, organisations, and places referred to in the review
- CSentiment analysis, which scores each review by the positive, negative, or neutral opinion it expresses Correct
- DSummarization, which condenses each review down into a shorter version of its overall content
Why A is wrong: Keyword extraction reads the same review text, which makes it tempting, but it only returns the salient terms and never judges whether the opinion expressed is favourable or not.
Why B is wrong: Entity detection identifies typed things such as brand or product names, so it looks plausible for review text, but it reports what is mentioned rather than the writer's opinion about it.
Why C is correct: Sentiment analysis evaluates the emotional tone of a piece of text and returns a positive, negative, or neutral classification, which is exactly what is needed to flag dissatisfied customers.
Why D is wrong: Summarization shortens the text while keeping its meaning, which sounds useful, but a shorter review is still not labelled by opinion polarity, so the team could not rank unhappy customers.