This project focuses on analyzing the perception, historical relevance, entities involved, and fashion preferences of luxury brands Louis Vuitton, Chanel, Dior, and Versace based on articles from top fashion magazines. Using Natural Language Processing (NLP) techniques and sentiment analysis, the project extracts and analyzes data to understand how these brands are portrayed in the media.
- Python: Main programming language used for data analysis and processing.
- Libraries: BeautifulSoup, numpy, pandas, nltk, wordcloud, matplotlib, PIL, plotly.
- Techniques: Sentiment analysis, text mining, entity recognition, data visualization.
- Data Collection: Scraping articles from Vogue, Elle, Glamour, and Harper's Bazaar.
- Exploratory Data Analysis (EDA): Analyzing dominant adjectives, historical words, entities, clothing items, and fabrics associated with each brand.
- Sentiment Analysis: Evaluating positive and negative perceptions in fashion narratives.
- Data Visualization: Creating visual representations including word clouds and sentiment distributions.
- Conclusion: Summarizing findings on brand perceptions and preferences in fashion magazines.
To run the project locally, follow these steps:
-
Clone the repository:
git clone https://github.com/tuusuario/tuproject.git cd tuproject -
Install dependencies:
pip install -r requirements.txt -
Run Jupyter Notebook:
jupyter notebook
- Notebook Files: Explore
analysis.ipynbfor detailed code and analysis. - Data Files: All data files used in the analysis are stored in the
data/directory.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE file for details.