SFMS — Student Feedback Management System

Collect, analyze and visualize student feedback from an interactive Python CLI

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📖 Overview

SFMS is a menu-driven command-line tool for managing student course feedback. Every piece of feedback submitted through the CLI is automatically analyzed — sentiment polarity and subjectivity via TextBlob, emotion tags via keyword extraction, and key phrases — then stored in a simple flat file and displayed in polished tables. An advanced sentiment summary and a rating-distribution pie chart turn raw comments into quick, presentable insight.

No database, no server — just Python, a text file, and your terminal.

✨ Features

Interactive 7-option menu:

#	Action	What it does
1	Add Feedback	Enter registration no, student, course, comment and rating — sentiment, emotions and key phrases are extracted automatically
2	Delete Feedback	Remove a record by registration number or student name
3	Search Feedback	Look up records by registration number or student name
4	Display All Feedback	Full tabular view (tabulate) of every record
5	Advanced Sentiment Analysis	Aggregate sentiment breakdown across all feedback
6	Rating Distribution	matplotlib pie chart of rating frequencies
7	Exit	Saves and quits

NLP pipeline (runs on every submission)

• Sentiment — TextBlob polarity scored and classified: > 0.1 Positive · < -0.1 Negative · otherwise Neutral, with the numeric score and subjectivity stored
• Emotions — positive / negative / neutral keyword matching ("excellent", "boring", "average"…)
• Key phrases — the three most substantial sentences from the comment

🗂️ Data Storage

Everything persists to student_feedback.txt (auto-created), a pipe-separated (|) table:

Column	Description
Registration_No · Student_Name · Course	Who and what
Feedback	The free-text comment
Sentiment · Sentiment_Score · Subjectivity	TextBlob analysis results
Emotions · Key_Phrases	Extracted tags
Rating · Date	Numeric rating and timestamp

🚀 Setup

python -m venv .venv
# Windows:
.venv\Scripts\activate
# macOS/Linux:
source .venv/bin/activate

pip install pandas tabulate textblob matplotlib numpy python-dateutil pytz tzdata
python -m textblob.download_corpora

nltk, click, joblib, regex, tqdm and colorama are pulled in automatically as transitive dependencies of the core packages above.

▶️ Usage

python student_feedback_manager.py

Then follow the menu prompts — add a few feedback entries and try options 4–6 to see the table, sentiment summary and rating chart.

📁 Project Structure

SFMS/
├── student_feedback_manager.py   # The entire application (CLI, NLP pipeline, charting)
├── student_feedback.txt          # Data file (created automatically on first save)
├── LICENSE                       # MIT
└── README.md

🔧 Implementation Notes

• StudentFeedbackManager class wraps load/save (pandas CSV with | separator), so data survives restarts and stays human-editable
• Sentiment thresholds (±0.1) keep clearly negative comments from being masked by neutral text
• The pie chart renders in a separate matplotlib window (plt.show())
• Records are kept sorted by registration number for stable display

📄 License

MIT — see LICENSE.

👤 Author

Prince Kumar — @princekumar-dev

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_{SFMS — student voices, scored and charted in one menu.}