sentiment-analysis

User feedback analysis processes CSV exports by extracting text, identifying at least three user segments, assigning sentiment scores from -1 to +1, and surfacing prioritized product recommendations based on frequency and business impact.

Survey responses undergo thematic synthesis to extract recurring themes, pain points, and positive feedback per segment. Representative quotes are cited alongside sentiment scores to highlight satisfaction drivers and prioritize issues.

Sentiment analysis handles large-scale inputs including PDFs, review datasets, and social listening outputs. It requires text extraction from files before computing sentiment scores and synthesizing thematic insights across detected user segments.

Product review datasets are processed to detect user segments, compute sentiment scores, and generate NPS proxies. The analysis prioritizes issues by frequency and business impact, yielding 2-3 high-impact product recommendations per segment.

Customer insights from support transcripts are prioritized by estimating satisfaction drivers and ranking issues according to frequency and business impact, producing segment-level profiles and actionable product recommendations.

Thematic analysis on mixed-format user feedback requires text extraction as a prerequisite. Processing large-scale inputs across CSVs, PDFs, and social listening outputs depends on data cleanliness to accurately detect segments and assign sentiment scores.