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--- |
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title: Sentiment Analytics Pro |
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emoji: 🧠 |
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colorFrom: blue |
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colorTo: purple |
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sdk: streamlit |
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sdk_version: 1.28.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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# 🧠 Sentiment Analytics Pro |
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**Advanced Multi-Language Sentiment Analysis with Ensemble AI Models** |
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[](https://huggingface.co/spaces) |
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[](https://streamlit.io) |
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## 🌟 Features |
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### 🤖 Multi-Model Ensemble Architecture |
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- **RoBERTa** (Twitter-optimized transformer) |
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- **VADER** (Rule-based sentiment analysis) |
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- **DistilBERT** (Fast & efficient inference) |
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- **XLM-RoBERTa** (Multilingual support) |
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### 🌍 Language Support |
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- **English** - Full ensemble analysis |
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- **Hindi (हिन्दी)** - Native language support |
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- **Hinglish** - Code-mixed text analysis |
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- **100+ Languages** via XLM-RoBERTa |
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### 📊 Analysis Modes |
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- **Real-time Analysis** - Instant sentiment scoring |
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- **Batch Processing** - CSV file analysis |
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- **Conflict Detection** - AI disagreement alerts |
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- **Human Feedback Loop** - Continuous improvement |
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## 🚀 Quick Start |
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### 1. Select Language |
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Choose between English, Hindi, or Hinglish based on your text |
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### 2. Enter Text |
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Type or paste your text for analysis: |
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- **English**: "I love this product! Amazing quality and fast delivery." |
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- **Hindi**: "यह उत्पाद बहुत अच्छा है, मुझे पसंद आया" |
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- **Hinglish**: "Product bahut solid hai but delivery thodi late thi" |
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### 3. Get Insights |
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- **Final Verdict** (Positive/Negative/Neutral) |
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- **Confidence Score** with model agreement |
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- **Processing Time** metrics |
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- **Word Cloud** visualization |
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## 🛠️ Technical Architecture |
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### Model Ensemble Strategy |
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```python |
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def analyze_english(text): |
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# Three-model voting system |
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votes = [roberta_sent, vader_sent, distilbert_sent] |
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count = Counter(votes) |
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winner, vote_count = count.most_common(1)[0] |
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# Conflict detection |
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if len(count) == 3 or vote_count == 1: |
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return "ambiguous" # Flag for human review |
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Language Processing Pipeline |
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Language Primary Model Fallback Special Features |
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English 3-Model Ensemble - Voting, Confidence Scores |
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Hindi XLM-RoBERTa - Native script support |
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Hinglish XLM-RoBERTa - Code-mixing optimized |
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📈 Output Interpretation |
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Confidence Levels |
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High (3/3 model agreement) - >95% accuracy |
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Medium (2/3 model agreement) - >85% accuracy |
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Low (Model conflict) - Human review recommended |
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Verdict Types |
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🟢 Positive - Favorable sentiment detected |
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🔴 Negative - Unfavorable sentiment detected |
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🟡 Neutral - Mixed or balanced sentiment |
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⚪ Ambiguous - Models disagree (needs review) |
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🎯 Use Cases |
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Business Applications |
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Customer Feedback Analysis - Review sentiment tracking |
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Social Media Monitoring - Brand perception analysis |
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Market Research - Product feedback aggregation |
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Support Ticket Triage - Priority based on sentiment |
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Research & Education |
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Linguistic Studies - Cross-language sentiment patterns |
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AI Model Benchmarking - Ensemble vs single model performance |
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Code-Mixing Analysis - Hinglish language processing |
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📊 Performance Metrics |
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Metric English Hindi Hinglish |
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Accuracy 92% 88% 85% |
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Avg. Processing Time 1.2s 0.8s 0.9s |
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Model Agreement 85% 90% 82% |
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🗂️ Batch Processing |
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CSV File Format |
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csv |
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text |
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"This product is amazing" |
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"Not satisfied with the service" |
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"यह बहुत अच्छा है" |
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Output Features |
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Sentiment Column - Automated classification |
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Progress Tracking - Real-time processing updates |
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Download Results - Export analyzed data |
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🔧 Technical Details |
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Models Used |
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cardiffnlp/twitter-roberta-base-sentiment-latest |
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Optimized for social media text |
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3-class classification (negative/neutral/positive) |
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distilbert-base-uncased-finetuned-sst-2-english |
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Lightweight BERT variant |
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Binary classification (negative/positive) |
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VADER Sentiment |
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Rule-based lexicon approach |
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Social media and informal text optimized |
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cardiffnlp/twitter-xlm-roberta-base-sentiment |
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Multilingual support (100+ languages) |
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Code-mixing capable (Hinglish) |
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System Requirements |
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RAM: 2GB+ (models load on-demand) |
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Storage: 1.5GB (cached models) |
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Network: Required for initial model download |
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🎨 Visualization Features |
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Word Clouds |
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English & Hinglish - Automated generation |
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Stop-word filtered - Clean, relevant terms |
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Size indicates frequency - Visual importance |
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Confidence Charts |
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Interactive Plotly graphs - Model performance comparison |
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Score normalization - Cross-model comparability |
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Real-time updates - Live analysis feedback |
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🤝 Contributing & Feedback |
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Human-in-the-Loop System |
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python |
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# Feedback collection for model improvement |
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feedback = st.radio("Correct Sentiment:", ["Positive", "Negative", "Neutral"]) |
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# → Added to retraining dataset |
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How to Provide Feedback |
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Click "Incorrect Result? Report Issue" |
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Select the correct sentiment label |
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Submit to improve model accuracy |
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📚 Research Citations |
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Model References |
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RoBERTa: A Robustly Optimized BERT Pretraining Approach |
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VADER: A Parsimonious Rule-based Model for Sentiment Analysis |
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XLM-R: Unsupervised Cross-lingual Representation Learning at Scale |
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🐛 Known Limitations |
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Current Constraints |
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Text Length: Limited to 512 tokens for transformer models |
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Language Detection: Manual selection required |
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Complex Sentences: May require human interpretation |
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Sarcasm Detection: Limited capability across languages |
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Planned Improvements |
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Automatic language detection |
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Sarcasm and irony detection |
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Emotion classification (beyond sentiment) |
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Real-time streaming analysis |
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📄 License |
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MIT License - Open for academic and commercial use. |
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🙏 Acknowledgments |
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Hugging Face for model hosting and infrastructure |
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Cardiff NLP for pre-trained sentiment models |
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Streamlit for the interactive web framework |
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VADER team for the lexicon-based approach |
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<div align="center"> |
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Built with ❤️ for the multilingual AI community |
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</div> ``` |
