README.md

metadata

license: mit
language:
  - en
metrics:
  - accuracy
library_name: sklearn
pipeline_tag: text-classification
tags:
  - code

Model Training

The sentiment analysis model is trained using a Support Vector Machine (SVM) classifier with a linear kernel. The cleaned text data is transformed into a bag-of-words representation using the CountVectorizer. The trained model is saved as Sentiment_classifier_model.joblib, and the corresponding TF-IDF vectorizer is saved as vectorizer_model.joblib.

Download the Vectorizer model first and load the model :

Usage :

from huggingface_hub import hf_hub_download
import joblib
from sklearn.preprocessing import LabelEncoder

# Download and load the sentiment analysis model from Hugging Face Model Hub
model = joblib.load(hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib"))

# Load the TF-IDF vectorizer
tfidf_vectorizer = joblib.load(hf_hub_download("DineshKumar1329/Sentiment_Analysis", "vectorizer_model.joblib"))

def clean_text(text):
    return text.lower()

def predict_sentiment(user_input):
    """Predicts sentiment for a given user input."""
    cleaned_text = clean_text(user_input)
    input_matrix = tfidf_vectorizer.transform([cleaned_text])
    prediction = model.predict(input_matrix)[0]

    if isinstance(model.classes_, LabelEncoder):
        prediction = model.classes_.inverse_transform([prediction])[0]

    return prediction

# Get user input
user_input = input("Enter a sentence: ")

# Predict sentiment
predicted_sentiment = predict_sentiment(user_input)

# Output the prediction
print(f"Predicted Sentiment: {predicted_sentiment}")