{"id":22062611,"url":"https://github.com/raminBadri/Textmining-python","last_synced_at":"2026-02-06T21:00:58.387Z","repository":{"id":260902565,"uuid":"276203186","full_name":"raminBadri/Textmining-python","owner":"raminBadri","description":"A text/document mining with python","archived":false,"fork":false,"pushed_at":"2025-12-23T12:16:14.000Z","size":4042,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-01-30T08:32:43.591Z","etag":null,"topics":["data-science","k-means-clustering","pycharm","python3","text-mining"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/raminBadri.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2020-06-30T20:36:18.000Z","updated_at":"2025-12-23T12:16:18.000Z","dependencies_parsed_at":"2025-01-28T23:41:58.438Z","dependency_job_id":"1ad4706a-7ef8-485d-8793-02afbeac8627","html_url":"https://github.com/raminBadri/Textmining-python","commit_stats":null,"previous_names":["raminbadri/python-datamining","raminbadri/textmining-python"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/raminBadri/Textmining-python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raminBadri%2FTextmining-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raminBadri%2FTextmining-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raminBadri%2FTextmining-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raminBadri%2FTextmining-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/raminBadri","download_url":"https://codeload.github.com/raminBadri/Textmining-python/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raminBadri%2FTextmining-python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29175821,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-06T20:14:21.878Z","status":"ssl_error","status_checked_at":"2026-02-06T20:14:21.443Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","k-means-clustering","pycharm","python3","text-mining"],"created_at":"2024-11-30T18:25:38.148Z","updated_at":"2026-02-06T21:00:58.376Z","avatar_url":"https://github.com/raminBadri.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Text Mining and Customer Clustering with TripAdvisor Reviews\n\n[![Python](https://img.shields.io/badge/Python-3.8%2B-blue)](https://www.python.org)  \n[![Pandas](https://img.shields.io/badge/Pandas-Used-green)](https://pandas.pydata.org)  \n[![Scikit-learn](https://img.shields.io/badge/Scikit--learn-Used-orange)](https://scikit-learn.org)  \n[![Matplotlib](https://img.shields.io/badge/Matplotlib-Used-red)](https://matplotlib.org)  \n[![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)  \n\nThis is one of my master's projects, focusing on the practice of **text mining** using a dataset inspired by the **TripAdvisor** website. The primary aim is to **cluster customers** based on their **comments** and reviews left on the platform. The project was implemented using **Python 3** and the **PyCharm IDE**.\n\nDue to data sharing restrictions, the original dataset cannot be included, but all steps, data features, and methodologies are thoroughly documented in the provided report file.\n\n## 📊 About the Dataset\n\nThe dataset is inspired by **TripAdvisor**, a well-known travel agency company. It includes **1,850 textual data entries** (documents), each containing reviews and ratings from previous travelers regarding various hotels. To optimize performance and reduce computational load, only the **first 100 documents** were randomly selected as the input data. This subset maintains the essence of the original dataset without following any specific order.\n\n- **Data Type**: Textual reviews and ratings.\n- **Source**: TripAdvisor (anonymized and inspired).\n- **Size**: 100 documents (subset from 1,850).\n- **Purpose**: Clustering based on customer feedback.\n\n## 🗺️ Project Sections\n\nThe project is structured into the following sections, as detailed in the report:\n\n1. **Section 1: General Objectives of the Chapter**  \n   Outlines the overall goals and scope of the text mining project.\n\n2. **Section 2: Dataset Characteristics**  \n   Describes the dataset's features, structure, and key attributes.\n\n3. **Section 3: Programming Language and IDE Description**  \n   Details the use of Python 3 and PyCharm IDE for implementation.\n\n4. **Section 4: Data Cleansing and Clustering Phase**  \n   Covers data preprocessing, cleansing techniques, and the application of clustering algorithms.\n\n5. **Section 5: Presentation and Analysis of Results**  \n   Presents the clustering outcomes, visualizations, and analytical insights.\n\n## 🛠️ Technologies and Packages\n\nThe project leverages the following Python packages for data manipulation, analysis, and visualization:\n\n- **Pandas**: For data handling and manipulation.\n- **NumPy**: For numerical computations.\n- **Scikit-learn (sklearn)**: For machine learning algorithms, including clustering.\n- **Matplotlib**: For plotting and visualizations.\n- **SciPy**: For scientific computing, supporting advanced mathematical functions.\n\n## 🔍 Clustering Methods\n\nTwo primary clustering techniques were employed to group customers based on their textual comments:\n\n- **K-Means Clustering**: A centroid-based algorithm that partitions data into k clusters by minimizing variance within each cluster.\n- **Dendrogram (Hierarchical Clustering)**: Used for visualizing the hierarchical structure of clusters, often via linkage methods to show relationships between data points.\n\n## 📁 Repository Structure\n\n- `report/`: Detailed project report and documentation.\n- `src/`: Source code files for data processing and clustering.\n- `README.md`: This file.\n\n## 🚀 Getting Started\n\n1. Ensure you have **Python 3.8+** installed. Download from [python.org](https://www.python.org).\n2. Install the required packages:  \n   ```bash\n   pip install pandas numpy scikit-learn matplotlib scipy\n   ```\n3. Clone this repository:  \n   ```bash\n   git clone https://github.com/raminBadri/Text-Mining-TripAdvisor.git\n   ```\n4. Open the project in **PyCharm IDE** or run the scripts/notebooks to explore the text mining and clustering processes.\n\n\u003e **Note**: The dataset is not provided; refer to the report for data characteristics and use a similar textual dataset for experimentation.\n\n## 🤝 Contributing\n\nContributions are welcome! Feel free to fork this repository, suggest improvements, or add new analyses. Please submit pull requests with detailed descriptions.\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n*If this project inspires your research or studies, please give it a ⭐ and share your feedback!* 🚀  \n\nFor more details, check the full report in the `reports/` directory. If you have questions, open an issue in this repository.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FraminBadri%2FTextmining-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FraminBadri%2FTextmining-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FraminBadri%2FTextmining-python/lists"}