Sentiment Analysis Models

This repository contains multiple trained sentiment analysis models for binary sentiment classification (Positive / Negative).

Repository Structure

Sentiment/
│
├── BERT/
├── Bi_LSTM/
├── Linear_Svm/
├── Logistic_Regression/
├── Lstm/
├── Naive Bayes/
├── XGBoost/
└── Notebooks/

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Available Models

• BERT
• LSTM
• Bi-LSTM
• Logistic Regression
• Linear SVM
• Naive Bayes
• XGBoost

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Using the BERT Model

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Sudheer17/Sentiment/BERT"
)

result = classifier("I love this movie!")

print(result)

Example Output

[{'label': 'Positive', 'score': 0.9848}]

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Example Inputs

I love this movie!

This is the worst experience ever.

The service was average.

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Notes

• The BERT model can be loaded directly using the Hugging Face Transformers library.
• Classical Machine Learning and Deep Learning models are stored in their respective folders.
• Use the appropriate tokenizer and preprocessing pipeline when working with non-BERT models.

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Requirements

transformers
torch
tensorflow
scikit-learn
joblib
numpy
pandas

Install them using:

pip install transformers torch tensorflow scikit-learn joblib numpy pandas

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License

This repository is released under the MIT License.