https://github.com/zen204/accenture-tech-news-summarization-engine
A tool developed to analyze knowledge graphs from technology news articles, uncovering insights and trends about technology products, platforms, services, and their industry impact. Built during an internship at Accenture to inform decision-making in the tech landscape.
https://github.com/zen204/accenture-tech-news-summarization-engine
data-analysis decision-making graph-visualization industry-insights jupyter-notebook knowledge-graph machine-learning python tech-news tech-trends
Last synced: 3 months ago
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A tool developed to analyze knowledge graphs from technology news articles, uncovering insights and trends about technology products, platforms, services, and their industry impact. Built during an internship at Accenture to inform decision-making in the tech landscape.
- Host: GitHub
- URL: https://github.com/zen204/accenture-tech-news-summarization-engine
- Owner: Zen204
- Created: 2024-12-12T21:27:11.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-26T16:24:27.000Z (6 months ago)
- Last Synced: 2025-02-08T09:25:43.459Z (4 months ago)
- Topics: data-analysis, decision-making, graph-visualization, industry-insights, jupyter-notebook, knowledge-graph, machine-learning, python, tech-news, tech-trends
- Language: Jupyter Notebook
- Homepage:
- Size: 3.78 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Tech Insights Analyzer
Welcome to the **Tech Insights Analyzer** repository! This tool was developed as part of an internship project at Accenture to analyze knowledge graphs constructed from technology news articles. The aim is to uncover insights about technology products, platforms, services, and their impact across various industries. By extracting meaningful information and trends, the tool helps inform decision-making in the tech landscape.
---
## Features
- **Knowledge Graph Analysis**: Process and analyze structured data derived from technology news articles.
- **Trend Identification**: Extract patterns and trends about technology products and their influence across industries.
- **Industry Impact Insights**: Provide insights into how technologies affect different sectors.
- **Visualization Tools**: Generate graphs and charts to visualize trends and relationships within the knowledge graph.
---
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/tech-insights-analyzer.git
cd tech-insights-analyzer
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Ensure you have Jupyter Notebook installed:
```bash
pip install notebook
```
---
## Usage
1. **Run the Jupyter Notebook**:
```bash
jupyter notebook
```
Open the `Tech_News_Sum_ML.ipynb` file in your browser.
2. **Load Your Data**: Follow the instructions in the notebook to load the dataset of technology news articles and construct the knowledge graph.
3. **Analyze the Graph**: Use the provided tools to uncover insights, visualize trends, and generate reports.
---
## Dataset
This tool processes technology news datasets containing:
- Articles related to technology products, platforms, and services.
- Metadata such as publication date, source, and tags.
Ensure the dataset is structured appropriately before analysis. Example formats include JSON, CSV, or direct database connections.
---
## Example Output
- **Trends**: Identify growing technologies and their adoption in various industries.
- **Industry Impact**: Discover which sectors are most influenced by emerging technologies.
- **Product Insights**: Uncover patterns related to specific technology products or services.
---
## Contributing
We welcome contributions to enhance the tool! To contribute:
1. Fork this repository.
2. Create a new branch for your feature or fix.
3. Submit a pull request with a clear description of your changes.
---
## License
This project is licensed under the Accenture License.
---
## Team Members
- **Janae Perez**
- **Zen Edwards**
- **Phuong Ngyuen**
- **Lillian Tran**
- **Wanru Shao**
---
## Acknowledgments
This project was developed during an internship at Accenture. Thank you to my mentors and team members for their guidance and support throughout the development process!