| 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 | |