This hybrid sentiment analysis model combines Sentence Transformers for semantic embeddings with XGBoost for classification. Trained on Reddit comments for multiclass sentiment analysis: Negative, Positive, and Neutral.

Architecture

Input Text → SentenceTransformer → Embeddings (768D) →
Feature Engineering (Length + Sentiment + POS) → XGBoost → Prediction

Quick Start

python

import pickle
import numpy as np
from sentence_transformers import SentenceTransformer
from textblob import TextBlob
import nltk
from huggingface_hub import hf_hub_download

# Download NLTK data
nltk.download('punkt', quiet=True)
nltk.download('averaged_perceptron_tagger', quiet=True)

# Load models
xgb_path = hf_hub_download(repo_id="USERNAME/sentimental_analysis_updated", filename="xgboost_model.pkl")
sentence_path = hf_hub_download(repo_id="USERNAME/sentimental_analysis_updated", filename="sentence_transformer")

# Load XGBoost model
with open(xgb_path, 'rb') as f:
    pipeline_data = pickle.load(f)
    xgb_model = pipeline_data['xgboost_model']
    label_names = pipeline_data['label_names']

# Load SentenceTransformer
sentence_model = SentenceTransformer(sentence_path)

def predict_sentiment(text):
    # Extract features
    embedding = sentence_model.encode([text])
    comment_length = np.array([len(text.split())]).reshape(-1, 1)
    sentiment_polarity = np.array([TextBlob(text).sentiment.polarity]).reshape(-1, 1)

    # POS counts
    try:
        tags = nltk.pos_tag(nltk.word_tokenize(text))
        pos_counts = np.array([[
            sum(1 for _, tag in tags if tag.startswith('J')),  # Adjectives
            sum(1 for _, tag in tags if tag.startswith('N')),  # Nouns
            sum(1 for _, tag in tags if tag.startswith('V'))   # Verbs
        ]])
    except:
        pos_counts = np.array([[0, 0, 0]])

    # Combine features
    features = np.hstack([embedding, comment_length, sentiment_polarity, pos_counts])

    # Predict
    prediction = xgb_model.predict(features)[0]
    confidence = xgb_model.predict_proba(features)[0].max()

    return {
        'label': label_names[prediction],
        'confidence': confidence,
        'prediction_id': int(prediction)
    }

# Example usage
result = predict_sentiment("I love this new phone! It's amazing!")
print(f"Sentiment: {result['label']} (confidence: {result['confidence']:.3f})")

Model Details

Base Model: paraphrase-mpnet-base-v2
Classifier: XGBoost with GPU acceleration
Features: 772 dimensions (768 embeddings + 4 engineered)
Classes: 0=Negative, 1=Positive, 2=Neutral
Training Data: Reddit comments
Test Accuracy: 0.9966

Training Configuration

XGBoost Parameters: n_estimators=300, learning_rate=0.05, max_depth=6
Features: Embeddings + Comment Length + TextBlob Sentiment + POS Counts
Class Balancing: Sample weights for imbalanced data
Validation: Stratified train/val/test split

Citation

bibtex

@misc{reddit-sentiment-hybrid,
  title={Reddit Sentiment Analysis - Hybrid Model},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/USERNAME/sentimental_analysis_updated}
}

License

MIT License

mahekgheewala/sentimental_analysis_updated

Reddit Sentiment Analysis - Hybrid Model

Model Description