{"product_id":"mira-text-sentiment-analyst-ai-skill","title":"Mira — Text \u0026 Sentiment Analyst AI Skill","description":"\u003cdiv style=\"font-family: 'DM Sans', sans-serif; color: #1A1A18; max-width: 680px;\"\u003e\n\n  \u003cp style=\"font-size: 16px; font-weight: 600; color: #1A1A18; line-height: 1.5; margin: 0 0 8px 0;\"\u003e\n    Drop Mira into Claude and get a Text \u0026amp; Sentiment Analyst who identifies themes, codes qualitative data, assesses sentiment by topic and intensity, analyses customer reviews, processes interview transcripts, and backs every finding with specific examples from the text — without requiring you to know NLP or Python.\n  \u003c\/p\u003e\n\n  \u003cp style=\"font-size: 13px; font-weight: 400; color: #555550; line-height: 1.7; margin: 0 0 28px 0;\"\u003e\n    Text data is rarely uniformly positive or negative — customers who love the product but hate the delivery, employees who value colleagues but dislike management. Mira is one of the most evidence-cited AI text analysis tools available in Claude format — she never asserts a theme or sentiment without showing where in the data it comes from. Every claim requires 3–5 specific examples from the source text. She explains the analytical methodology used (thematic analysis, sentiment scoring, content analysis) so findings are understood rather than just accepted, and she identifies what is absent from a text as clearly as what is present.\n  \u003c\/p\u003e\n\n  \u003cdiv style=\"background: #E8F6F9; border-radius: 12px; padding: 24px 28px; margin-bottom: 24px;\"\u003e\n    \u003cp style=\"font-size: 10px; font-weight: 600; color: #1A8FA8; letter-spacing: 0.08em; text-transform: uppercase; margin: 0 0 16px 0;\"\u003eWhat you get\u003c\/p\u003e\n    \u003cul style=\"margin: 0; padding: 0; list-style: none;\"\u003e\n      \u003cli style=\"font-size: 13px; color: #1A1A18; padding: 7px 0; border-bottom: 1px solid rgba(26,143,168,0.12); display: flex; align-items: flex-start; gap: 10px;\"\u003e\n        \u003cspan style=\"color: #1A8FA8; font-weight: 600; flex-shrink: 0;\"\u003e→\u003c\/span\u003e\n        \u003cspan\u003eSentiment analysis with examples — overall positive, negative, neutral, and mixed sentiment assessment, sentiment by topic and theme within a document, intensity rating (strongly negative vs mildly negative), emotional tone identification (anger, satisfaction, frustration, delight), and customer review sentiment broken down by product, service, or attribute\u003c\/span\u003e\n      \u003c\/li\u003e\n      \u003cli style=\"font-size: 13px; color: #1A1A18; padding: 7px 0; border-bottom: 1px solid rgba(26,143,168,0.12); display: flex; align-items: flex-start; gap: 10px;\"\u003e\n        \u003cspan style=\"color: #1A8FA8; font-weight: 600; flex-shrink: 0;\"\u003e→\u003c\/span\u003e\n        \u003cspan\u003eThematic analysis — recurring themes identified and systematically coded using Braun \u0026amp; Clarke methodology, theme hierarchies built (themes and sub-themes), prominence and frequency assessed, and a priori vs emergent coding applied depending on whether the analytical framework is pre-existing or generated from the data\u003c\/span\u003e\n      \u003c\/li\u003e\n      \u003cli style=\"font-size: 13px; color: #1A1A18; padding: 7px 0; border-bottom: 1px solid rgba(26,143,168,0.12); display: flex; align-items: flex-start; gap: 10px;\"\u003e\n        \u003cspan style=\"color: #1A8FA8; font-weight: 600; flex-shrink: 0;\"\u003e→\u003c\/span\u003e\n        \u003cspan\u003eQualitative research data analysis — interview and focus group transcript analysis, thematic coding across multiple interviews, pattern identification, key findings synthesised in structured write-up format, and qualitative research findings presented as evidence-cited, publishable-quality analysis\u003c\/span\u003e\n      \u003c\/li\u003e\n      \u003cli style=\"font-size: 13px; color: #1A1A18; padding: 7px 0; border-bottom: 1px solid rgba(26,143,168,0.12); display: flex; align-items: flex-start; gap: 10px;\"\u003e\n        \u003cspan style=\"color: #1A8FA8; font-weight: 600; flex-shrink: 0;\"\u003e→\u003c\/span\u003e\n        \u003cspan\u003eCustomer and employee feedback analysis — NPS open-ended comment analysis, CSAT verbatim breakdowns, employee survey open-ended response analysis, social media comment sentiment, and customer review theme identification with specific quotes supporting each finding\u003c\/span\u003e\n      \u003c\/li\u003e\n      \u003cli style=\"font-size: 13px; color: #1A1A18; padding: 7px 0; border-bottom: 1px solid rgba(26,143,168,0.12); display: flex; align-items: flex-start; gap: 10px;\"\u003e\n        \u003cspan style=\"color: #1A8FA8; font-weight: 600; flex-shrink: 0;\"\u003e→\u003c\/span\u003e\n        \u003cspan\u003eContent and discourse analysis — key term and phrase frequency analysis, topic framing identification (how a subject is presented, not just what is said), persuasive and rhetorical technique identification, brand language and tone analysis, and what is absent from a text as well as what is present\u003c\/span\u003e\n      \u003c\/li\u003e\n      \u003cli style=\"font-size: 13px; color: #1A1A18; padding: 7px 0; display: flex; align-items: flex-start; gap: 10px;\"\u003e\n        \u003cspan style=\"color: #1A8FA8; font-weight: 600; flex-shrink: 0;\"\u003e→\u003c\/span\u003e\n        \u003cspan\u003eApplied text analysis across industries — media and press coverage analysis, political and policy language analysis, contract and legal language review, competitive communications analysis, and analytical framework and sampling strategy design for large datasets that exceed what can be processed directly in a single session\u003c\/span\u003e\n      \u003c\/li\u003e\n    \u003c\/ul\u003e\n  \u003c\/div\u003e\n\n  \u003cdiv style=\"display: flex; align-items: center; gap: 20px; background: #FFFFFF; border: 1px solid #E8E6E0; border-radius: 8px; padding: 14px 20px; margin-bottom: 24px;\"\u003e\n    \u003cspan style=\"font-size: 11px; color: #888780; font-family: monospace;\"\u003e📄 mira-text-analyst.md\u003c\/span\u003e\n    \u003cdiv style=\"width: 1px; height: 16px; background: #E8E6E0;\"\u003e\u003c\/div\u003e\n    \u003cspan style=\"font-size: 11px; color: #888780;\"\u003eUnder 2 min install\u003c\/span\u003e\n    \u003cdiv style=\"width: 1px; height: 16px; background: #E8E6E0;\"\u003e\u003c\/div\u003e\n    \u003cspan style=\"font-size: 11px; color: #888780;\"\u003eWorks with Claude Sonnet 4 \u0026amp; Claude Cowork\u003c\/span\u003e\n  \u003c\/div\u003e\n\n  \u003cdiv style=\"border-left: 3px solid #1A8FA8; padding-left: 16px;\"\u003e\n    \u003cp style=\"font-size: 10px; font-weight: 600; color: #1A8FA8; letter-spacing: 0.08em; text-transform: uppercase; margin: 0 0 6px 0;\"\u003eHow to install\u003c\/p\u003e\n    \u003cp style=\"font-size: 12px; color: #555550; line-height: 1.7; margin: 0;\"\u003e\n      Download the .md file → open Claude → paste the file content into your system prompt or Project instructions → paste the text, reviews, transcripts, or feedback you need analysed → Mira identifies themes, assesses sentiment, and produces evidence-cited findings instantly. Paste the content directly — for very large datasets she designs the analytical framework and sampling approach.\n    \u003c\/p\u003e\n  \u003c\/div\u003e\n\n\u003c\/div\u003e","brand":"Kissmyskills","offers":[{"title":"Default Title","offer_id":57640127824136,"sku":null,"price":29.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1036\/1444\/7880\/files\/11_mira-text-analyst.png?v=1776869013","url":"https:\/\/kissmyskills.com\/products\/mira-text-sentiment-analyst-ai-skill","provider":"KissMySkills","version":"1.0","type":"link"}