sentiment-analysis
by phurynThe sentiment-analysis skill turns user feedback into segment-level insights, sentiment scores, JTBD, and product impact. Use it for sentiment-analysis for Data Analysis on reviews, surveys, support notes, or social listening when you need a practical sentiment-analysis guide, not a generic polarity check.
This skill scores 78/100, which means it is a solid listing candidate for directory users who need a ready-made sentiment-analysis workflow. The repository gives enough operational detail to install with confidence, though it would still benefit from more adoption aids and supporting assets.
- Clear triggerability: the frontmatter says to use it for user feedback, surveys, reviews, and social listening data.
- Good operational structure: it outlines a step-by-step workflow for ingestion, segmenting, thematic analysis, sentiment scoring, and impact assessment.
- Low guesswork for core use case: the body is substantial and includes constraints and a defined analytic goal focused on segment-level insights.
- No support files or references are included, so users must trust the single SKILL.md for execution guidance.
- No install command or example inputs/outputs are provided, which may slow first-time adoption.
Overview of sentiment-analysis skill
What sentiment-analysis does
The sentiment-analysis skill helps you turn raw feedback into segment-level insight: who is happy, who is frustrated, what themes repeat, and which issues matter most. It is built for analyzing user feedback at scale, not for generic opinion labeling. If you need a sentiment-analysis skill that can summarize reviews, survey responses, social listening exports, or support notes into usable product signals, this is a strong fit.
Who should install it
Install this sentiment-analysis skill if you work on product research, UX, CX, growth, or market analysis and need faster synthesis than a manual spreadsheet review. It is especially useful for sentiment-analysis for Data Analysis when the goal is to connect sentiment with segments, JTBD, and business impact rather than produce a single polarity score.
What makes it useful
The main differentiator is the workflow focus: it asks for segment identification, thematic analysis, sentiment scoring, and impact ranking in one pass. That structure reduces the common failure mode of shallow sentiment summaries that miss why people feel a certain way or which subgroup is affected.
How to Use sentiment-analysis skill
Install and locate the skill
Use the sentiment-analysis install flow from your skills manager, then open the skill folder in phuryn/pm-skills at pm-market-research/skills/sentiment-analysis. Start with SKILL.md, because it contains the operating instructions the model should follow. Since this repo has no helper scripts or reference folders, the skill file is the primary source of truth.
Give it the right input
The sentiment-analysis usage pattern works best when you provide actual feedback data plus a clear analysis goal. Strong inputs name the source, scope, and decision context, for example: Analyze these 1,200 app reviews for churn risk by segment and summarize top pain points by sentiment. Weak inputs like Do sentiment analysis on this leave the model guessing about audience, granularity, and output format.
Shape the prompt for better output
A good sentiment-analysis guide prompt should specify:
- the data type: CSV, survey text, reviews, tickets, or interview notes
- the unit of analysis: customer, segment, topic, or time period
- the output you need: themes, sentiment scores, JTBD, or prioritization
- any constraints: timeframe, language mix, product area, or minimum segment count
If your source is messy, ask the skill to first build an inventory of files or rows, then synthesize. That improves traceability and makes the final summary easier to trust.
Suggested workflow
- Gather the feedback set and remove obvious duplicates.
- State the business question before asking for analysis.
- Ask for segment-level output instead of one global verdict.
- Review the first pass for missing segments, then rerun with tighter instructions.
- Use the result to decide what to fix, validate, or explore next.
sentiment-analysis skill FAQ
Is this better than a normal prompt?
Usually yes, if you want a repeatable analysis structure. A plain prompt can work for one-off summaries, but the sentiment-analysis skill is better when you need consistent segment detection, explicit scoring, and a clearer path from feedback to product action.
What inputs does it handle best?
It is best for written feedback with enough context to infer themes: reviews, surveys, open-text responses, research notes, and social posts. It can still help with shorter text, but sparse inputs make segment and JTBD inference less reliable.
When should I not use it?
Do not use it if you only need a simple positive/negative count, if your data is too small to support segmentation, or if the source is mostly structured metrics with little text. In those cases, a lighter analysis prompt or a spreadsheet method may be faster.
Is it beginner-friendly?
Yes, if you can describe the feedback source and the question you want answered. The main challenge is not the skill itself but giving enough context to avoid vague synthesis. Beginners get better results when they specify audience, timeframe, and desired output upfront.
How to Improve sentiment-analysis skill
Make the analysis question narrower
The fastest way to improve sentiment-analysis output is to narrow the target. Ask about one product area, one customer group, or one decision at a time. For example, Analyze onboarding survey comments from new SMB users and identify the top 5 negative themes by segment will produce more actionable results than Summarize customer sentiment.
Provide evidence-rich samples
Better input means better segmentation. Include representative comments, not just totals, and keep metadata that helps the model separate groups, such as plan type, channel, region, or lifecycle stage. For sentiment-analysis for Data Analysis, even a small amount of labeled context can improve the usefulness of sentiment scoring.
Watch for common failure modes
The most common misses are overgeneralized themes, forced sentiment labels on ambiguous comments, and weak prioritization. If the first pass feels broad, ask for: fewer segments, direct quotes supporting each theme, and a clearer ranking by frequency and business impact.
Iterate after the first pass
Use the first output as a draft map, then refine with follow-up prompts such as: Re-run this with only enterprise accounts, Separate complaints about pricing from complaints about UX, or Add a shortlist of the highest-impact fixes. That iterative loop usually produces more decision-ready sentiment-analysis guidance than a single broad prompt.