Arrow Review the passage about the correct business and classify its meaning with a documented rubric. Separate positive, neutral, negative and mixed statements, and distinguish factual errors from evaluative language. Sentiment in a sampled AI answer describes that answer; it is not a direct measurement of customer opinion or brand reputation across the market.
Keep the surrounding context
A response may recommend a product for one task while warning that it is unsuitable for another. Labelling the entire answer negative loses the buying condition that makes it useful. Preserve the full passage and identify the specific claim being assessed.
Check entity matching before interpretation. Similar company names, old brands or products with overlapping names can produce a false sentiment signal. Where an automated classifier is used, include a review process for uncertain and mixed cases.
Choose the appropriate response to the finding
A verifiable factual error may require correcting a source page or requesting a third-party update. A reasonable product limitation should be represented accurately, not removed merely because it sounds unfavorable. Unsupported criticism needs investigation of both the answer and any linked evidence.
Track recurring issues under comparable questions and conditions. Infrequent wording changes should not automatically trigger a content overhaul. Confirm whether sentiment analysis is actually included in a vendor’s plan rather than inferring it from general visibility tracking.
What to check
- Verify that the passage concerns the correct brand or product.
- Use a rubric that allows mixed and uncertain classifications.
- Separate source corrections from legitimate product limitations.
A practical example
An assistant says a product suits small teams but lacks a documented enterprise feature. The reviewer records a conditional recommendation and checks the feature claim rather than marking the whole answer as negative.
Sources and further reading
These steps are Arrow AI's implementation guidance. The example is illustrative; it is not a measured customer result. Publication and source access do not guarantee an AI recommendation.
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