NYXAR Logistic Sentiment Model
Model Description
A sentiment classification model developed for the NYXAR AI Intelligence & Observability Platform.
The model predicts sentiment across three classes:
- Positive
- Neutral
- Negative
Framework:
- Scikit-Learn
- TF-IDF Vectorization
- Logistic Regression
Language:
- English
License:
- MIT
Training Data
Dataset:
- SetFit/amazon_reviews_multi_en
The dataset contains English Amazon product reviews used for sentiment classification.
Intended Use
This model is designed for:
- Customer feedback analysis
- Product review monitoring
- Support ticket intelligence
- Sentiment trend analysis
Limitations
The model may struggle with:
- Sarcasm
- Irony
- Domain-specific terminology
- Very long texts
Predictions should be used as supporting signals rather than business-critical decisions.
Performance
Metrics obtained during evaluation:
- Accuracy: 69.28%
- Precision: 65.75%
- Recall: 69.28%
- F1 Score: 65.89%
Usage
import joblib
model = joblib.load("logistic_model_v1.pkl")
vectorizer = joblib.load("tfidf_vectorizer_v1.pkl")
text = ["The product exceeded expectations."]
X = vectorizer.transform(text)
prediction = model.predict(X)
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