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:
This project is designed as a basic example for understanding the complete machine learning model lifecycle:
1 Dataset
2 ↓
3 Text Preprocessing
4 ↓
5 TF-IDF Vectorization
6 ↓
7 Logistic Regression
8 ↓
9 Model Evaluation
10 ↓
11 Model Serialization
12 ↓
13 Hugging Face Hub
14 ↓
15 Inference
Model Details
Property Description Model Type Text Classification Task Sentiment Analysis Algorithm Logistic Regression Feature Extraction TF-IDF Library Scikit-learn Language Python Classes Positive / Negative Model Format Pickle (.pkl)
Model Architecture
The model consists of two primary components:
1. TF-IDF Vectorizer
The input text is converted into numerical features using TF-IDF.
1 Input Text
2 ↓
3 TF-IDF Vectorizer
4 ↓
5 Numerical Feature Vector
2. Logistic Regression
The numerical feature vector is passed to the Logistic Regression classifier.
1 TF-IDF Features
2 ↓
3 Logistic Regression
4 ↓
5 Sentiment Prediction
Complete pipeline:
1 Text
2 ↓
3 Lowercase Conversion
4 ↓
5 Tokenization
6 ↓
7 Stop Word Removal
8 ↓
9 TF-IDF
10 ↓
11 Logistic Regression
12 ↓
13 Prediction
Sentiment Labels
The model uses the following labels:
Label Sentiment 0Negative 1Positive
Example:
1 Input:
2 "I love this product"
3
4 Output:
5 1 → Positive
Another example:
1 Input:
2 "This product is terrible"
3
4 Output:
5 0 → Negative
Installation
Clone or download the project.
Install the required Python libraries:
pip install -r requirements.txtRequired packages:
1 scikit-learn
2 pandas
3 joblib
4 huggingface_hub
Training the Model
Run the training script:
The training process performs the following steps:
1 1. Load training data
2 2. Split data into training and testing sets
3 3. Create TF-IDF vectorizer
4 4. Transform text into numerical features
5 5. Train Logistic Regression
6 6. Evaluate model
7 7. Save trained model
The trained model will be saved as:
model/sentiment_model.pklLocal Prediction
After training the model, run:
Example input:
I really love this productExample output:
1 Prediction: positive
2 Confidence: 0.72
Another example:
Output:
1 Prediction: negative
2 Confidence: 0.69
Using the Model
The saved model can be loaded using joblib.
1 import joblib
2
3 model = joblib . load (
4 "model/sentiment_model.pkl"
5 )
6
7 text = [
8 "I love this product"
9 ]
10
11 prediction = model . predict ( text )
12
13 if prediction [ 0 ] == 1 :
14 print ( "Positive" )
15 else :
16 print ( "Negative" )
Prediction With Confidence
The model can also return prediction probabilities.
1 import joblib
2
3 model = joblib . load (
4 "model/sentiment_model.pkl"
5 )
6
7 text = [
8 "This is an amazing product"
9 ]
10
11 prediction = model . predict ( text )
12 probability = model . predict_proba ( text )
13
14 label = "Positive" if prediction [ 0 ] == 1 else "Negative"
15 confidence = max ( probability [ 0 ] )
16
17 print ( "Sentiment:" , label )
18 print ( "Confidence:" , round ( confidence , 4 ) )
Loading the Model From Hugging Face
Install the Hugging Face Hub library:
pip install huggingface_hubDownload the model:
1 from huggingface_hub import hf_hub_download
2 import joblib
3
4 model_path = hf_hub_download (
5 repo_id = "YOUR_USERNAME/basic-sentiment-model" ,
6 filename = "sentiment_model.pkl"
7 )
8
9 model = joblib . load ( model_path )
10
11 text = [
12 "This product is fantastic"
13 ]
14
15 prediction = model . predict ( text )
16
17 print ( prediction )
Replace:
with your Hugging Face username.
Example Predictions
Input Prediction I love this product Positive This product is excellent Positive Amazing experience Positive Very good service Positive I am very happy Positive I hate this product Negative This product is terrible Negative Very bad experience Negative Worst service Negative I am very disappointed Negative
Project Structure
1 basic-sentiment-model/
2 │
3 ├── model/
4 │ └── sentiment_model.pkl
5 │
6 ├── train.py
7 ├── predict.py
8 ├── upload.py
9 ├── requirements.txt
10 ├── README.md
11 └── .gitignore
Training Dataset
The current model uses a small demonstration dataset containing positive and negative sentences.
Example positive samples:
1 I love this product
2 This product is excellent
3 Amazing experience
4 Very good service
5 I am very happy
Example negative samples:
1 I hate this product
2 This product is terrible
3 Very bad experience
4 Worst service
5 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.
1 from sklearn . metrics import accuracy_score
2
3 accuracy = accuracy_score (
4 y_test ,
5 predictions
6 )
7
8 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:
1 TF-IDF
2 N-grams
3 Word Embeddings
4 Sentence Embeddings
Machine Learning Algorithms
Compare:
1 Logistic Regression
2 Naive Bayes
3 SVM
4 Random Forest
5 Gradient Boosting
Deep Learning
The project can later be upgraded to:
1 Neural Network
2 ↓
3 LSTM
4 ↓
5 GRU
6 ↓
7 Transformer
8 ↓
9 BERT
Hugging Face Transformers
A future version can use a pretrained Transformer model for improved NLP performance.
Hugging Face Deployment
The trained model can be uploaded to the Hugging Face Hub.
Example:
Then:
hf upload YOUR_USERNAME/basic-sentiment-model ./model .The model will be available at:
YOUR_USERNAME/basic-sentiment-modelIntended 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.
Author
Basic Sentiment Analysis Model
Built with:
1 Python
2 Scikit-learn
3 TF-IDF
4 Logistic Regression
5 Hugging Face Hub
Acknowledgements
This project uses the open-source Python machine learning ecosystem, particularly:
Scikit-learn
Pandas
Joblib
Hugging Face Hub
