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 \u003cimg src=\"/public/streamlit_hero.jpg\" alt=\"CustomerInsight\" width=\"80\"\u003e\n  \u003ch1\u003eCustomerInsight\u003c/h1\u003e\n  \u003cp\u003eAI-powered customer review analysis platform\u003c/p\u003e\n\n  \u003ca href=\"https://www.python.org/\"\u003e\u003cimg src=\"https://img.shields.io/badge/python-3.9+-blue.svg\" alt=\"Python 3.9+\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/ChanMeng666/customer-insight/blob/main/LICENSE\"\u003e\u003cimg src=\"https://img.shields.io/github/license/ChanMeng666/customer-insight\" alt=\"License\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/ChanMeng666/customer-insight/stargazers\"\u003e\u003cimg src=\"https://img.shields.io/github/stars/ChanMeng666/customer-insight\" alt=\"Stars\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://chanmeng666-customerinsight-app-oeiu2h.streamlit.app/\"\u003e\u003cstrong\u003eLive Demo →\u003c/strong\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n---\n\n## Overview\n\nCustomerInsight is an interactive analytics platform that uses NLP and machine learning to extract actionable insights from customer reviews. Built with Streamlit, it provides real-time sentiment analysis, keyword extraction, topic modeling, and anomaly detection — optimized for both Chinese and English text.\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/user-attachments/assets/8914f8fd-53fd-4c42-b330-a0a5f2100f0e\" alt=\"Dashboard\" width=\"800\"\u003e\n\u003c/div\u003e\n\n## Features\n\n- **Sentiment Analysis** — BERT-based sentiment classification with confidence scores. Uses `roberta-base-finetuned-jd-binary-chinese` for Chinese and `bert-base-multilingual-uncased-sentiment` for English.\n- **Keyword Extraction** — TF-IDF keyword extraction with word cloud visualization, trend analysis, and rating-based comparison.\n- **Topic Modeling** — LDA and K-Means clustering with topic network graphs, heatmaps, and trend tracking.\n- **Anomaly Detection** — Isolation Forest-based outlier detection with multi-feature analysis (rating, text length, sentiment).\n- **Interactive Filtering** — Filter by date range, rating, text length, and keywords with real-time updates.\n- **Data Export** — Download filtered data and analysis results as CSV.\n\n## Quick Start\n\n```bash\n# Clone the repository\ngit clone https://github.com/ChanMeng666/customer-insight.git\ncd customer-insight\n\n# Install dependencies\npip install -r requirements.txt\n\n# Run the application\nstreamlit run app.py\n```\n\nThen open http://localhost:8501 and upload the sample dataset from `data/example_dataset.csv`.\n\n## Data Format\n\n| Column | Type | Required | Description |\n|--------|------|----------|-------------|\n| `timestamp` | datetime | Yes | Review timestamp |\n| `content` | string | Yes | Review text |\n| `rating` | float | No | Rating (1-5 scale) |\n| `category` | string | No | Review category |\n| `user_id` | string | No | User identifier |\n\n## Architecture\n\n```mermaid\ngraph TD\n    A[CSV/Excel Upload] --\u003e B[Data Processor]\n    B --\u003e C[Text Cleaning]\n    C --\u003e D{Analysis Engine}\n    D --\u003e E[Sentiment Analyzer]\n    D --\u003e F[Keyword Analyzer]\n    D --\u003e G[Topic Analyzer]\n    D --\u003e H[Insight Analyzer]\n    E --\u003e I[Visualization Layer]\n    F --\u003e I\n    G --\u003e I\n    H --\u003e I\n    I --\u003e J[Streamlit Dashboard]\n```\n\n## Tech Stack\n\n**Framework**: Streamlit · **Language**: Python 3.9+\n\n**NLP \u0026 ML**: Transformers · PyTorch · jieba · NLTK · scikit-learn · Sentence Transformers\n\n**Visualization**: Plotly · Matplotlib · WordCloud · NetworkX\n\n**Data**: Pandas · NumPy · SciPy\n\n## Contributing\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for the development guide.\n\n## License\n\n[MIT](LICENSE) © [Chan Meng](https://github.com/ChanMeng666)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchanmeng666%2Fcustomer-insight","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchanmeng666%2Fcustomer-insight","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchanmeng666%2Fcustomer-insight/lists"}