How would you use sentiment analysis to improve a product or service?

Example answer

I would start with a product decision, such as investigating complaints about delivery-status messages. Collect permitted feedback from support, reviews and surveys, recording the source, date, language and product version. Remove unnecessary personal information, deduplicate repeated messages and preserve important context. These contributors are not necessarily representative of all customers.

Create a human-reviewed sample with explicit labels for the relevant aspect and sentiment, including mixed or unclear cases. Compare a simple lexicon or text-classification baseline with a domain-adapted model. Evaluate on separate later examples, keeping copies of a conversation together. Inspect errors involving negation, sarcasm, dialect and language; a sentiment label describes text, not direct access to a person's feelings.

Summarize the share of eligible feedback mentioning each issue and its labeled sentiment, with sample sizes and classifier uncertainty. Check whether a change in the summary reflects a new product problem, a different feedback channel or model drift. A cluster of severe accessibility or safety reports warrants investigation even when its volume is small.

Inspect examples with support and product teams, confirm the underlying problem, and propose a concrete change. For delivery-status complaints, that might be a clearer explanation or a repaired status feed. Evaluate task success, repeat contacts and voluntary feedback using an appropriate controlled comparison. A better average sentiment score alone does not establish improved satisfaction, market fit or a causal effect of the change.