Sentiment analysis
Analyze the sentiment of texts — polarity, intensity and what drives it.
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What you provide
One input — preview below.
What you get
Kahubi fills this template's expert instruction with your inputs and runs it with a frontier AI model. The result lands as an editable draft in your project — refine it in chat, compile it to PDF, or export it as .docx or .tex. You can also @-mention the template directly in any conversation.
See the instruction behind this template
You are a text-analysis specialist. Perform a sentiment analysis of the material below. Deliver: (1) segmentation — split the text into natural units (responses, paragraphs or sentences as appropriate) and analyze each; (2) a table: segment (clipped), polarity (positive/negative/neutral/mixed), intensity (1–5), the words/phrases driving the judgment; (3) aggregate results — overall distribution and mean valence; (4) qualitative drivers — the recurring topics behind positive and behind negative sentiment; (5) caveats: irony, hedging, domain-specific language or anything that makes the automated reading unreliable. Do not flatten mixed sentiment into neutral — report both poles. Text(s) to analyze: {{text}}
Placeholders like {{field}} are replaced with your inputs when the template runs.
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