feat(workshops): add NLP sentiment analysis tutorial notebook

beginner/README.md

@@ -24,6 +24,12 @@
 - **Prerequisites:** Python and basic statistics.
 - **Interactive Lab:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AIMLCLUBOCT/Workshops/blob/main/beginner/notebooks/03_intro_machine_learning.ipynb) [`03_intro_machine_learning.ipynb`](./notebooks/03_intro_machine_learning.ipynb)
 
+### 4. NLP & Sentiment Analysis: Text Classification with Scikit-Learn
+- **Topics:** Text cleaning, tokenization, TF-IDF feature extraction, Multinomial Naive Bayes, and interactive model inference.
+- **Duration:** 2.5 Hours
+- **Prerequisites:** Python and introductory classification concepts.
+- **Interactive Lab:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AIMLCLUBOCT/Workshops/blob/main/beginner/notebooks/04_nlp_sentiment_analysis.ipynb) [`04_nlp_sentiment_analysis.ipynb`](./notebooks/04_nlp_sentiment_analysis.ipynb)
+
 ---
 
 ## 🚀 How to Run

beginner/notebooks/04_nlp_sentiment_analysis.ipynb

@@ -0,0 +1,217 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# \ud83d\udcdd Workshop 4: Natural Language Processing & Sentiment Analysis Starter\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AIMLCLUBOCT/Workshops/blob/main/beginner/notebooks/04_nlp_sentiment_analysis.ipynb)\n",
+    "\n",
+    "**AI & Machine Learning Club \u2013 Oriental College of Technology, Bhopal**\n",
+    "\n",
+    "Welcome to this hands-on NLP tutorial! In this lab, you will learn:\n",
+    "1. Text preprocessing: cleaning, normalizing, and tokenizing natural language.\n",
+    "2. How **TF-IDF (Term Frequency-Inverse Document Frequency)** turns words into mathematical feature vectors.\n",
+    "3. Training a **Multinomial Naive Bayes** classifier for sentiment prediction.\n",
+    "4. Evaluating model performance using Confusion Matrix & Classification Report.\n",
+    "5. Live interactive inference on custom sentences."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup & Environment Dependencies\n",
+    "Let's import the necessary libraries (`scikit-learn`, `pandas`, `numpy`, and `matplotlib`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "print('Libraries imported successfully!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Load Sample Sentiment Dataset\n",
+    "We use a dataset containing balanced positive and negative reviews."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "    # Positive reviews (1)\n",
+    "    ('This AI workshop was extremely informative and well-structured!', 1),\n",
+    "    ('The practical coding demonstrations helped clarify the core concepts.', 1),\n",
+    "    ('Brilliant hands-on lab! I learned so much about neural networks.', 1),\n",
+    "    ('The mentors were patient, encouraging, and answered all questions.', 1),\n",
+    "    ('Loved the step-by-step approach to machine learning fundamentals.', 1),\n",
+    "    ('The project starter code was clean, reproducible, and easy to run.', 1),\n",
+    "    ('Great session! Really looking forward to the next hackathon.', 1),\n",
+    "    ('Outstanding explanations of linear algebra and loss functions.', 1),\n",
+    "    ('I highly recommend this club workshop to all engineering students.', 1),\n",
+    "    ('Awesome experience building my first sentiment classification model!', 1),\n",
+    "    # Negative reviews (0)\n",
+    "    ('The pacing was way too fast and hard to follow for beginners.', 0),\n",
+    "    ('Disappointed with the sound quality in the seminar hall.', 0),\n",
+    "    ('Too much theory on slides and not enough interactive coding time.', 0),\n",
+    "    ('The prerequisites were not clearly stated and I felt completely lost.', 0),\n",
+    "    ('The example code had errors and failed to run on my local machine.', 0),\n",
+    "    ('The workshop started late and ended before covering all topics.', 0),\n",
+    "    ('Unhelpful explanations, it felt rushed and disorganized.', 0),\n",
+    "    ('The presentation was boring and repetitive.', 0),\n",
+    "    ('Could not get the environment setup properly in time.', 0),\n",
+    "    ('Poor communication regarding the schedule and lab location.', 0)\n",
+    "]\n",
+    "\n",
+    "df = pd.DataFrame(data, columns=['review', 'sentiment'])\n",
+    "print(f'Total reviews: {len(df)}')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Text Preprocessing Function\n",
+    "Raw text data contains punctuation, irregular casing, and noise. We normalize text into clean tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_text(text: str) -> str:\n",
+    "    text = text.lower() # Lowercase\n",
+    "    text = re.sub(r'[^a-zA-Z0-9\\s]', ' ', text) # Strip punctuation\n",
+    "    text = re.sub(r'\\s+', ' ', text).strip() # Normalize whitespace\n",
+    "    return text\n",
+    "\n",
+    "df['cleaned_review'] = df['review'].apply(clean_text)\n",
+    "df[['review', 'cleaned_review']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Train / Test Split & TF-IDF Vectorization\n",
+    "We separate training data (75%) from holdout test data (25%) and vectorize using unigrams and bigrams."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    df['cleaned_review'], df['sentiment'], test_size=0.25, random_state=42, stratify=df['sentiment']\n",
+    ")\n",
+    "\n",
+    "vectorizer = TfidfVectorizer(ngram_range=(1, 2), stop_words='english')\n",
+    "X_train_vec = vectorizer.fit_transform(X_train)\n",
+    "X_test_vec = vectorizer.transform(X_test)\n",
+    "\n",
+    "print(f'Training shape: {X_train_vec.shape}')\n",
+    "print(f'Testing shape:  {X_test_vec.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Model Training & Evaluation\n",
+    "We fit a **Multinomial Naive Bayes** classifier and evaluate precision, recall, and the confusion matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = MultinomialNB(alpha=1.0)\n",
+    "model.fit(X_train_vec, y_train)\n",
+    "\n",
+    "preds = model.predict(X_test_vec)\n",
+    "print('Classification Report:')\n",
+    "print(classification_report(y_test, preds, target_names=['Negative', 'Positive']))\n",
+    "\n",
+    "# Display Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, preds)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Negative', 'Positive'])\n",
+    "disp.plot(cmap='Blues')\n",
+    "plt.title('Sentiment Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Interactive Live Inference\n",
+    "Test the classifier with your own review sentences!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_sentiment(text: str):\n",
+    "    cleaned = clean_text(text)\n",
+    "    vec = vectorizer.transform([cleaned])\n",
+    "    pred = model.predict(vec)[0]\n",
+    "    proba = model.predict_proba(vec)[0]\n",
+    "    sentiment = '\ud83d\ude0a Positive' if pred == 1 else '\ud83d\ude1e Negative'\n",
+    "    confidence = proba[pred] * 100\n",
+    "    print(f'Input:      \"{text}\"')\n",
+    "    print(f'Sentiment:  {sentiment} (Confidence: {confidence:.2f}%)\\n')\n",
+    "\n",
+    "predict_sentiment('The instructors explained every concept with fantastic examples!')\n",
+    "predict_sentiment('I had a terrible experience with the code errors and confusing slides.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. \ud83d\ude80 Student Extension Challenge\n",
+    "Now it's your turn to experiment!\n",
+    "- **Challenge 1:** Try comparing `MultinomialNB` with `LogisticRegression` or `LinearSVC`.\n",
+    "- **Challenge 2:** Inspect `vectorizer.get_feature_names_out()` and discover which words have the strongest TF-IDF weights.\n",
+    "- **Challenge 3:** Connect this classifier to a simple Streamlit interface in `beginner/`!"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}