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-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.
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-Live Demo
-Will be update here after deployment.
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-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
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-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.
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-Metrics
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-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.
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-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).
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-Limitations
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-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).
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-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