https://github.com/tahirzia-1/nlp-textclassify

A hands-on NLP project comparing classic ML models (Naïve Bayes, SVM, Logistic Regression) and ANNs for text classification using SMS Spam and 20 Newsgroups datasets.
https://github.com/tahirzia-1/nlp-textclassify

adam-optimizer ann cbow deep-learning lemmatization logistic-regression naive-bayes-classifier nlp nlp-machine-learning skipgram-algorithm svm tensorflow tfidf tfidf-vectorizer tokenization vectorization word2vec

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A hands-on NLP project comparing classic ML models (Naïve Bayes, SVM, Logistic Regression) and ANNs for text classification using SMS Spam and 20 Newsgroups datasets.

• Host: GitHub
• URL: https://github.com/tahirzia-1/nlp-textclassify
• Owner: TahirZia-1
• Created: 2025-02-25T10:44:59.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-02-25T10:50:54.000Z (7 months ago)
• Last Synced: 2025-02-25T11:37:29.295Z (7 months ago)
• Topics: adam-optimizer, ann, cbow, deep-learning, lemmatization, logistic-regression, naive-bayes-classifier, nlp, nlp-machine-learning, skipgram-algorithm, svm, tensorflow, tfidf, tfidf-vectorizer, tokenization, vectorization, word2vec
• Language: Jupyter Notebook
• Homepage:
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• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📝 Text Classification Report: SMS Spam Collection and 20 Newsgroups Datasets

## 🌟 1. Introduction

This report presents an implementation of **text classification** on two distinct datasets: the *SMS Spam Collection* dataset and the *20 Newsgroups* dataset. The objective is to preprocess textual data, extract features using **TF-IDF** and **Word2Vec**, and evaluate the performance of multiple classification models, including ***Naive Bayes***, ***Support Vector Machines (SVM)***, ***Logistic Regression***, and a ***Neural Network*** built with TensorFlow. The models are assessed using standard metrics such as *accuracy*, *precision*, *recall*, and *F1-score*, with results visualized for comparison.

- **SMS Spam Collection**: Binary-labeled text messages (*spam* or *ham*).

- **20 Newsgroups**: Multi-class dataset of news articles across 20 categories.

This analysis highlights the effectiveness of different approaches for **spam detection** (binary classification) and **news categorization** (multi-class classification).

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## 🛠️ 2. Methodology

### 2.1 Datasets

- **SMS Spam Collection**: Loaded from `spam.csv`, containing **5,572 messages** labeled as `ham` (0) or `spam` (1).

- **20 Newsgroups**: Fetched via `sklearn.datasets.fetch_20newsgroups`, containing **18,846 documents** across 20 categories, with headers, footers, and quotes removed.

### 2.2 Data Preprocessing

A preprocessing function (`preprocess_text`) was applied to both datasets:

- Converted text to *lowercase*.

- Removed special characters and numbers using regex (`re.sub`).

- Tokenized text with NLTK’s `word_tokenize`.

- Removed stopwords and lemmatized tokens using `WordNetLemmatizer`.

- Joined tokens into a cleaned string (`processed_text` column).

### 2.3 Dataset Splitting

Datasets were split into *training (60%)*, *validation (20%)*, and *test (20%)* sets using `prepare_dataset` with stratification:

- **SMS**: Train: *3,343*, Validation: *1,114*, Test: *1,115*.

- **Newsgroups**: Train: *11,307*, Validation: *3,769*, Test: *3,770*.

### 2.4 Feature Extraction

Two methods were used:

1. **TF-IDF Vectorization**:

   - *SMS*: `TfidfVectorizer` with 5,000 max features → `(3,343, 5,000)`.

   - *Newsgroups*: `TfidfVectorizer` with 10,000 max features → `(11,307, 10,000)`.

2. **Word2Vec Embeddings**:

   - Custom Word2Vec models trained on training corpus (`vector_size=100, window=5, min_count=1`).

   - Sentence embeddings as mean of word vectors → `(3,343, 100)` for SMS, `(11,307, 100)` for Newsgroups.

### 2.5 Models

Four models were implemented:

1. **Naive Bayes**: `MultinomialNB` (TF-IDF features).

2. **SVM**: `SVC` (TF-IDF features).

3. **Logistic Regression**: `LogisticRegression` (TF-IDF features).

4. **Neural Network**: TensorFlow `Sequential` model:

   - `Dense(256, ReLU)` → `Dropout(0.4)`.

   - `Dense(128, ReLU)` → `Dropout(0.3)`.

   - `Dense(64, ReLU)` → `Dropout(0.2)`.

   - Output: `Dense(2, sigmoid)` (SMS); `Dense(20, softmax)` (Newsgroups).

   - Trained for *100 epochs* with *Adam* optimizer.

### 2.6 Evaluation Metrics

A custom `evaluate_model` function computed:

- *Accuracy*, *Precision*, *Recall*, *F1-Score* (weighted for multi-class).

- *Sensitivity* and *Specificity* (binary classification).

- *Classification Report* and *Confusion Matrix*.

- *ROC-AUC* (binary classification).

---

## 📊 3. Results

### 3.1 SMS Spam Detection

Performance on the SMS test set (*1,115 samples*):

| **Model**            | **Accuracy** | **Precision** | **Recall** | **F1-Score** |

|-----------------------|--------------|---------------|------------|--------------|

| Naive Bayes          | 0.9614       | **0.9908**    | 0.7200     | 0.8340       |

| **SVM**              | **0.9839**   | 0.9714        | **0.9670** | **0.9379**   |

| Logistic Regression  | 0.9534       | 0.9712        | 0.6733     | 0.7953       |

| Neural Network       | 0.9193       | 0.7000        | 0.7000     | 0.7000       |

- **Best Model**: *SVM* with highest accuracy (0.9839) and balanced F1-score (0.9379).

- **Observations**: Naive Bayes led in precision; SVM excelled overall. Neural Network underperformed.

### 3.2 20 Newsgroups Classification

Performance on the Newsgroups test set (*3,770 samples*):

| **Model**            | **Accuracy** | **Precision** | **Recall** | **F1-Score** |

|-----------------------|--------------|---------------|------------|--------------|

| Naive Bayes          | 0.7037       | **0.7233**    | 0.7037     | 0.6911       |

| SVM                  | 0.6997       | 0.7122        | 0.6997     | 0.7008       |

| **Logistic Regression** | **0.7090** | 0.7159        | **0.7090** | **0.7065**   |

| Neural Network       | 0.4459       | 0.4763        | 0.4459     | 0.4349       |

- **Best Model**: *Logistic Regression* with highest accuracy (0.7090), edging Naive Bayes by 0.0053.

- **Observations**: Neural Network lagged significantly (0.4459); traditional models performed consistently (~0.70).

### 3.3 Neural Network Training (Newsgroups)

- *Training Accuracy*: 0.1271 (Epoch 1) → 0.4550 (Epoch 100).

- *Validation Accuracy*: Peaked at 0.4582 (Epoch 89), final 0.4460.

- *Loss*: Stabilized at ~1.65–1.68, suggesting limited generalization.

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## 🎨 4. Visualizations

- **Training History (Newsgroups NN)**: Accuracy/loss plots showed convergence; validation plateaued after ~50 epochs.

- **Model Comparison Bar Charts**:

  - *SMS*: SVM dominated.

  - *Newsgroups*: Logistic Regression and Naive Bayes led; Neural Network trailed.

---

## 🏁 5. Conclusion

This analysis successfully implemented text classification on the *SMS Spam Collection* and *20 Newsgroups* datasets. Key findings:

- **SMS Spam Detection**: *SVM* was most accurate (0.9839), excelling with TF-IDF features.

- **20 Newsgroups Classification**: *Logistic Regression* led (0.7090), narrowly beating Naive Bayes (0.7037).

- **Neural Network**: Underperformed (0.4459 for Newsgroups), possibly due to tuning or complexity.

### 💡 Recommendations

- Tune Neural Network hyperparameters or use pre-trained embeddings (*e.g., GloVe*).

- Explore ensemble methods for model synergy.

- Increase Word2Vec `vector_size` for richer embeddings.

This study showcases the strengths of traditional ML (*SVM*, *Logistic Regression*) versus deep learning for text classification.

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