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Title: The Hangry Student Decoder Team Name: HackBusters Track: 1 (UiPath Agent) Team Members: - Anubhaba Swain, 2306105 - Subhasmita Sendh, 23052679 - Neelanjana Dutta, 2305713 - Arko Ghosh, 2306266 Slides: 1. **Title Slide** - The Hangry Student Decoder - HackBusters | Track 1 (UiPath Agent) - Team Members listed 2. **Problem Statement & Objective** - Problem: Rohan, the college Mess Secretary, struggles to interpret emotional, slang-filled student feedback containing Hinglish and emojis. - Objective: Build a UiPath-based NLP Agent that converts Gen-Z feedback into structured data showing Category, Sentiment (with sarcasm detection), and Justification. 3. **Methodology** - Step 1: Data Collection (Student feedback samples) - Step 2: Text Cleaning (Slang removal, Hinglish translation, emoji normalization) - Step 3: NLP Model Training (Category + Sentiment) - Step 4: UiPath Integration for automation - Step 5: Output visualization dashboard 4. **Technologies/Tools Used** - UiPath Studio - Python (Transformers, NLTK, spaCy) - Google Translate API - Pandas & Streamlit - Fuzzy Matching 5. **Results** - 92% accuracy in category detection - 85% precision in sarcasm sentiment detection - 70% reduction in manual review time - Real-time structured report generation 6. **Conclusion** - Automates messy feedback decoding - Improves mess decision-making - Combines NLP and UiPath for intelligent automation 7. **Future Scope** - Voice feedback support - Multilingual expansion (Odia, Hindi, English) - Real-time chatbot integration - Continuous learning for accuracy improvement
HackBusters' UiPath NLP agent decodes Gen-Z Hinglish feedback for college mess secretary, extracting category, sentiment (incl. sarcasm), and justification. Uses Python NLP, translation APIs; achieves
This title slide presents "The Hangry Student Decoder" by team HackBusters in Track 1 (UiPath Agent). It lists the team members: Anubhaba Swain (2306105), Subhasmita Sendh (23052679), Neelanjana Dutta (2305713), and Arko Ghosh (2306266).
HackBusters | Track 1 (UiPath Agent) Anubhaba Swain (2306105), Subhasmita Sendh (23052679), Neelanjana Dutta (2305713), Arko Ghosh (2306266)
The slide's left column outlines the problem: Rohan, the college Mess Secretary, struggles to interpret emotional, slang-filled student feedback in Hinglish with emojis. The right column states the objective: build a UiPath-based NLP Agent to convert Gen-Z feedback into structured data with Category, Sentiment (including sarcasm detection), and Justification.
| Problem | Objective |
|---|---|
| Rohan, the college Mess Secretary, struggles to interpret emotional, slang-filled student feedback containing Hinglish and emojis. | Build a UiPath-based NLP Agent that converts Gen-Z feedback into structured data: Category, Sentiment (with sarcasm detection), and Justification. |
The slide outlines a five-phase methodology workflow for processing student feedback: Data Collection via surveys, Text Cleaning with slang/emoji handling, NLP Model Training for category/sentiment analysis including sarcasm, UiPath Integration for automation, and Output Visualization via dashboards. Tools include Python (NLTK, Transformers, spaCy, Pandas), Google Translate API, UiPath Studio, and Streamlit.
{ "headers": [ "Phase", "Description", "Tools/Technologies" ], "rows": [ [ "Data Collection", "Student feedback samples", "Manual/Surveys" ], [ "Text Cleaning", "Slang removal, Hinglish translation, emoji normalization", "Python (NLTK), Google Translate API" ], [ "NLP Model Training", "Category + Sentiment (with sarcasm detection)", "Python (Transformers, spaCy)" ], [ "UiPath Integration", "Automation of text processing and analysis pipeline", "UiPath Studio, Fuzzy Matching" ], [ "Output Visualization", "Dashboard for structured reports", "Pandas, Streamlit" ] ] }
Source: The Hangry Student Decoder - HackBusters
The "Technologies/Tools Used" slide features a grid highlighting key tools: UiPath Studio for NLP workflow automation, Python libraries (Transformers, NLTK, spaCy) for text analysis, and Google Translate API for converting Hinglish to English. It also includes Pandas & Streamlit for data handling and dashboards, plus Fuzzy Matching to improve accuracy on slang variations.
{ "features": [ { "icon": "š¤", "heading": "UiPath Studio", "description": "Automation platform orchestrating NLP workflows." }, { "icon": "š", "heading": "Python NLP Libraries", "description": "Transformers, NLTK, spaCy for text analysis." }, { "icon": "š", "heading": "Google Translate API", "description": "Converts Hinglish feedback to English." }, { "icon": "š", "heading": "Pandas & Streamlit", "description": "Data handling and dashboard visualization." }, { "icon": "š", "heading": "Fuzzy Matching", "description": "Boosts accuracy on slang variations." } ] }
Source: HackBusters
The Results slide highlights 92% accuracy in category detection and 85% precision in sarcasm detection. It also shows a 70% reduction in manual review time.
Precise identification of feedback categories
Effective sarcasm sentiment analysis
Drastic cut in review time
The conclusion slide summarizes key benefits: automating messy feedback decoding, improving mess decision-making, and combining NLP with UiPath for intelligent automation. It closes with a "Thank you!" subtitle.
ā¢ Automates messy feedback decoding
Thank you!
Source: HackBusters | The Hangry Student Decoder
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