vdrakopoulou

vdrakopoulou / Sentiment-Analysis_BUS2503_AI-for-Business

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H&M Seasonal Campaign Sentiment Analysis – Case Study & Replication Package for BUS2503 / AI for Business using KNIME, Python, and LLM ChatGPT as a co‑tutor.

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Found Feb 17, 2026 at 27 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

Educational materials for a university course case study on sentiment analysis of customer reviews for an H&M seasonal campaign, including a dataset, ready-to-run analysis templates, and no-code instructions.

How It Works

1
📚 Discover the class project

You hear about this handy folder in your AI for business class that has everything for analyzing opinions on H&M clothes campaigns.

2
📖 Read the story

You open the main guide to learn the business story behind customer comments and what questions to answer.

3
🚀 Pick your adventure

You choose an easy path that fits your style to crunch the numbers on happy, neutral, or grumpy comments.

4
✨ See the results shine

Everything runs smoothly, showing charts of how well it spots positive, neutral, or negative vibes in comments.

5
🔧 Tweak and learn

You play with settings or ask a smart helper for tips to make the opinion reader even smarter.

🎉 Ace your assignment

With pictures of your work and insights on customer feelings, you finish your report feeling like a pro.

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AI-Generated Review

What is Sentiment-Analysis_BUS2503_AI-for-Business?

This repo packages a full sentiment analysis pipeline for H&M seasonal campaign social media comments, helping businesses dissect reactions to seasonal sales, deals, personalized fashion recommendations, and roles like sales associates or product reviewers. Developers get a labeled dataset of platform-sourced reviews (Instagram, X, Facebook), a ready Python script with TF-IDF and logistic regression for quick accuracy checks, and KNIME drag-and-drop workflow steps for no-code runs. It includes a BUS2503 AI for Business case study with questions, plus tips for using ChatGPT as an LLM co-tutor to tweak models or interpret results.

Why is it gaining traction?

It bridges code and no-code worlds—run Python locally or in Colab, or replicate in KNIME without scripting—standing out from pure-code NLP tutorials. The H&M campaign context grounds abstract sentiment analysis in real business scenarios like seasonal hiring and sales feedback, with built-in evaluation metrics that show immediate results. ChatGPT integration as a prompt-ready assistant lowers the learning curve for experiments.

Who should use this?

Students tackling BUS2503-style AI for Business cases on H&M seasonal campaigns. Entry-level business analysts testing sentiment on marketing data without heavy coding. Instructors seeking plug-and-play materials for NLP intros, complete with datasets and no-code paths.

Verdict

Grab it for teaching or quick sentiment prototypes—strong docs and dual Python/KNIME support punch above its 27 stars and 1.0% credibility score. Still early-stage; fork and contribute to boost maturity.

(198 words)

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