This project applies natural language processing (NLP) to analyze 10-K filings from apparel and accessories firms. The analysis covers data cleaning, keyword extraction (Word Count & TF-IDF), Word2Vec embeddings, and sentiment analysis.
A comprehensive financial text analysis project that extracts meaningful insights from corporate filings using advanced NLP techniques.
Key Features:
- Data cleaning and preprocessing of financial documents
- Keyword extraction using Word Count and TF-IDF
- Word2Vec embeddings for semantic analysis
- Sentiment analysis of corporate communications
- Industry-specific insights for apparel and accessories sector
Technologies Used:
- Python
- Jupyter Notebook
- Natural Language Processing
- Word2Vec
- TF-IDF
- Sentiment Analysis
- Financial Data Analysis