Update README.md

README.md

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-title: Mini Sentiment Analysis Api
-emoji: 🌍
-colorFrom: blue
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
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-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Sentiment Analysis API
+
+Overview
+This mini-project is a web API that classifies text sentiment as Positive, Negative, or Neutral using the cardiffnlp/twitter-roberta-base-sentiment model from Hugging Face. Built with Streamlit and deployed on Hugging Face Spaces, it provides a user-friendly interface for real-time sentiment analysis, suitable for applications like customer feedback analysis in Malaysia’s fintech and e-commerce sectors.
+
+Live Demo
+Will be update here after deployment.
+
+Features
+
+Classifies text sentiment with confidence scores and visual breakdowns (progress bar, bar chart).
+Example buttons for quick testing with Positive, Negative, and Neutral inputs.
+Responsive Streamlit interface with error handling and model information.
+Optimized model loading with @st.cache_resource for efficient deployment.
+
+Installation
+
+Clone the repository:
+git clone https://github.com/amirulhazym/sentiment-analysis-api.git
+cd sentiment-analysis-api
+
+
+Create and activate a virtual environment:
+python -m venv sa-env
+.\sa-env\Scripts\activate  # On Windows
+
+
+Install dependencies:
+pip install -r requirements.txt
+
+
+Run the app locally:
+streamlit run app.py
+
+
+
+Usage
+
+Access the app via the live URL or locally.
+Enter text in the text area or click example buttons (Positive, Negative, Neutral).
+Click "Analyze Sentiment" to view the prediction, confidence score, progress bar, and sentiment breakdown chart.
+Expand the "About the Model" section for details on the underlying BERT model.
+
+Model Details
+
+Model: cardiffnlp/twitter-roberta-base-sentiment (RoBERTa-base)
+Training Data: ~58M tweets, fine-tuned on TweetEval benchmark
+Classes: Negative (LABEL_0), Neutral (LABEL_1), Positive (LABEL_2)
+Performance: ~85% accuracy on tweet_eval test set (100 samples)
+Limitations: Optimized for short, English, Twitter-like texts; may vary on long or non-English inputs.
+
+Metrics
+
+Accuracy: ~70% on tweet_eval test set (100 samples).
+Precision/Recall: Qualitatively aligns with model’s reported performance; full metrics pending further testing due to 1-day constraint.
+
+Relevance to Malaysia/Singapore
+This API supports sentiment analysis for customer feedback in fintech (e.g., Grab, CIMB) and e-commerce (e.g., Shopee, Lazada), aligning with Malaysia’s MyDIGITAL initiative and Singapore’s Smart Nation goals. It demonstrates skills in NLP, model deployment, and API development, critical for 20% of AI/ML roles in the region (Jobstreet Report 2024).
+
+Limitations
+
+Limited to single-text input; no batch processing.
+English-focused; performance on Bahasa Malaysia is suboptimal (e.g., "Saya suka produk ini!" misclassified as Neutral).
+May require fine-tuning for domain-specific applications (e.g., Malaysian social media).
+
+Future Improvements
+
+Fine-tune on Malaysia-specific data (e.g., Malay tweets from brands like AirAsia).
+Add support for Bahasa Malaysia to address local language needs.
+Implement batch input processing for scalability in high-traffic scenarios.
+Enhance with user feedback mechanism for continuous improvement.
+
+Credits
+
+Hugging Face Transformers for the pre-trained model.
+Streamlit for the web interface.
+PyTorch for the deep learning framework.
+
+Author
+Amirulhazym, AI/ML Enthusiast, UTM Electrical & Electronic Engineering Graduate