---
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`.

# Model Usage : 

from huggingface_hub import hf_hub_download
import joblib
model = joblib.load(
	hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
)
# only load pickle files from sources you trust
# read more about it here https://skops.readthedocs.io/en/stable/persistence.html

# Load the TF-IDF vectorizer used during training
tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your actual filename


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

# Clean the user input
cleaned_input = clean_text(user_input)

# Transform the cleaned text data using the TF-IDF vectorizer
input_matrix = tfidf_vectorizer.transform([cleaned_input])

# Make prediction
prediction = model.predict(input_matrix)[0]

# Display the prediction
print(f"Predicted Sentiment: {prediction}")
# Create a DataFrame with the results
df_result = pd.DataFrame({'User_Input': [user_input], 'Predicted_Sentiment': [prediction]})

# Save the DataFrame to an Excel file (append if the file already exists)
excel_filename = '/content/output_predictions.xlsx'  # Replace with your desired filename
try:
    # Load existing predictions from the Excel file
    df_existing = pd.read_excel(excel_filename)

    # Append the new predictions to the existing DataFrame
    df_combined = pd.concat([df_existing, df_result], ignore_index=True)

except FileNotFoundError:
    # If the file doesn't exist, create a new DataFrame
    df_combined = df_result

# Save the combined DataFrame to the Excel file
df_combined.to_excel(excel_filename, index=False)