basic-sentiment-model

---

library_name: scikit-learn tags:

• text-classification
• sentiment-analysis
• tfidf
• logistic-regression
• machine-learning
• nlp

---

Basic Sentiment Analysis Model

A simple Sentiment Analysis machine learning model built using TF-IDF (Term Frequency-Inverse Document Frequency) and Logistic Regression.

The model classifies text into two sentiment categories:

• Positive
• Negative

This project is designed as a basic example for understanding the complete machine learning model lifecycle:

Dataset
   ↓
Text Preprocessing
   ↓
TF-IDF Vectorization
   ↓
Logistic Regression
   ↓
Model Evaluation
   ↓
Model Serialization
   ↓
Hugging Face Hub
   ↓
Inference

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Model Details

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Model Architecture

The model consists of two primary components:

1. TF-IDF Vectorizer

The input text is converted into numerical features using TF-IDF.

Input Text
    ↓
TF-IDF Vectorizer
    ↓
Numerical Feature Vector

2. Logistic Regression

The numerical feature vector is passed to the Logistic Regression classifier.

TF-IDF Features
      ↓
Logistic Regression
      ↓
Sentiment Prediction

Complete pipeline:

Text
 ↓
Lowercase Conversion
 ↓
Tokenization
 ↓
Stop Word Removal
 ↓
TF-IDF
 ↓
Logistic Regression
 ↓
Prediction

---

Sentiment Labels

The model uses the following labels:

Example:

Input:
"I love this product"

Output:
1 → Positive

Another example:

Input:
"This product is terrible"

Output:
0 → Negative

---

Installation

Clone or download the project.

Install the required Python libraries:

pip install -r requirements.txt

Required packages:

scikit-learn
pandas
joblib
huggingface_hub

---

Training the Model

Run the training script:

python train.py

The training process performs the following steps:

1. Load training data
2. Split data into training and testing sets
3. Create TF-IDF vectorizer
4. Transform text into numerical features
5. Train Logistic Regression
6. Evaluate model
7. Save trained model

The trained model will be saved as:

model/sentiment_model.pkl

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Local Prediction

After training the model, run:

python predict.py

Example input:

I really love this product

Example output:

Prediction: positive
Confidence: 0.72

Another example:

This product is terrible

Output:

Prediction: negative
Confidence: 0.69

---

Using the Model

The saved model can be loaded using joblib.

import joblib

model = joblib.load(
    "model/sentiment_model.pkl"
)

text = [
    "I love this product"
]

prediction = model.predict(text)

if prediction[0] == 1:
    print("Positive")
else:
    print("Negative")

---

Prediction With Confidence

The model can also return prediction probabilities.

import joblib

model = joblib.load(
    "model/sentiment_model.pkl"
)

text = [
    "This is an amazing product"
]

prediction = model.predict(text)
probability = model.predict_proba(text)

label = "Positive" if prediction[0] == 1 else "Negative"
confidence = max(probability[0])

print("Sentiment:", label)
print("Confidence:", round(confidence, 4))

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Loading the Model From Hugging Face

Install the Hugging Face Hub library:

pip install huggingface_hub

Download the model:

from huggingface_hub import hf_hub_download
import joblib

model_path = hf_hub_download(
    repo_id="YOUR_USERNAME/basic-sentiment-model",
    filename="sentiment_model.pkl"
)

model = joblib.load(model_path)

text = [
    "This product is fantastic"
]

prediction = model.predict(text)

print(prediction)

Replace:

YOUR_USERNAME

with your Hugging Face username.

---

Example Predictions

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Project Structure

basic-sentiment-model/
│
├── model/
│   └── sentiment_model.pkl
│
├── train.py
├── predict.py
├── upload.py
├── requirements.txt
├── README.md
└── .gitignore

---

Training Dataset

The current model uses a small demonstration dataset containing positive and negative sentences.

Example positive samples:

I love this product
This product is excellent
Amazing experience
Very good service
I am very happy

Example negative samples:

I hate this product
This product is terrible
Very bad experience
Worst service
I am very disappointed

The dataset is intentionally small because this project is designed as an educational demonstration of model training and Hugging Face deployment.

---

Evaluation

The model is evaluated using classification accuracy.

from sklearn.metrics import accuracy_score

accuracy = accuracy_score(
    y_test,
    predictions
)

print("Accuracy:", accuracy)

Because the demonstration dataset is very small, the reported accuracy should not be interpreted as a measure of real-world performance.

---

Limitations

This model has several limitations:

• It is trained on a very small dataset.
• It only supports positive and negative sentiment.
• It may not understand sarcasm.
• It may not understand context-dependent sentiment.
• It may perform poorly on unseen vocabulary.
• It is not suitable for production use without additional training and validation.
• The confidence score should not be interpreted as a calibrated probability of correctness.

For production use, a significantly larger and more representative dataset should be used.

---

Future Improvements

The model can be improved by:

Dataset

Use a larger sentiment dataset containing thousands or millions of examples.

Feature Engineering

Experiment with:

TF-IDF
N-grams
Word Embeddings
Sentence Embeddings

Machine Learning Algorithms

Compare:

Logistic Regression
Naive Bayes
SVM
Random Forest
Gradient Boosting

Deep Learning

The project can later be upgraded to:

Neural Network
      ↓
LSTM
      ↓
GRU
      ↓
Transformer
      ↓
BERT

Hugging Face Transformers

A future version can use a pretrained Transformer model for improved NLP performance.

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Hugging Face Deployment

The trained model can be uploaded to the Hugging Face Hub.

Example:

hf auth login

Then:

hf upload YOUR_USERNAME/basic-sentiment-model ./model .

The model will be available at:

YOUR_USERNAME/basic-sentiment-model

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Intended Use

This model is intended for:

• Learning Machine Learning
• Learning NLP
• Understanding text classification
• Understanding TF-IDF
• Understanding Logistic Regression
• Learning model serialization
• Learning Hugging Face Hub deployment
• Educational demonstrations
• Experimentation

---

License

This project is provided for educational and experimental purposes.

If you reuse or modify this project, add an appropriate license according to your intended use.

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Author

Basic Sentiment Analysis Model

Built with:

Python
Scikit-learn
TF-IDF
Logistic Regression
Hugging Face Hub

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Acknowledgements

This project uses the open-source Python machine learning ecosystem, particularly:

• Scikit-learn
• Pandas
• Joblib
• Hugging Face Hub

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