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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: sklearn |
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pipeline_tag: text-classification |
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tags: |
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- code |
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--- |
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## Model Training |
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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`. |
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# Model Usage : |
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from huggingface_hub import hf_hub_download |
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import joblib |
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model = joblib.load( |
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hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib") |
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) |
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# only load pickle files from sources you trust |
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# read more about it here https://skops.readthedocs.io/en/stable/persistence.html |
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# Load the TF-IDF vectorizer used during training |
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tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib') # Replace with your actual filename |
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# Take user input |
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user_input = input("Enter a sentence: ") |
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# Clean the user input |
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cleaned_input = clean_text(user_input) |
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# Transform the cleaned text data using the TF-IDF vectorizer |
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input_matrix = tfidf_vectorizer.transform([cleaned_input]) |
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# Make prediction |
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prediction = model.predict(input_matrix)[0] |
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# Display the prediction |
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print(f"Predicted Sentiment: {prediction}") |
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# Create a DataFrame with the results |
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df_result = pd.DataFrame({'User_Input': [user_input], 'Predicted_Sentiment': [prediction]}) |
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# Save the DataFrame to an Excel file (append if the file already exists) |
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excel_filename = '/content/output_predictions.xlsx' # Replace with your desired filename |
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try: |
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# Load existing predictions from the Excel file |
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df_existing = pd.read_excel(excel_filename) |
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# Append the new predictions to the existing DataFrame |
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df_combined = pd.concat([df_existing, df_result], ignore_index=True) |
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except FileNotFoundError: |
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# If the file doesn't exist, create a new DataFrame |
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df_combined = df_result |
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# Save the combined DataFrame to the Excel file |
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df_combined.to_excel(excel_filename, index=False) |
