📊 Generalized Analysis of Text Data

🔍 Overview

This repo provides a comprehensive toolkit for analyzing text data using various AI and Natural Language Processing (NLP) techniques. It's designed to be a reference guide and inspiration for text analysis projects, offering insights into themes, sentiment, named entities, and more.

✨ Features

• 📥 Data Collection: Uses the 20 Newsgroups dataset for demonstration.
• 📝 Initial Textual Analysis: Performs basic text statistics and word frequency analysis.
• 🔬 Exploratory Data Analysis: Visualizes key aspects of the text data.
• 🗂️ Topic Modeling: Uncovers hidden thematic structures in the text corpus.
• 🧩 Text Clustering: Groups similar documents using K-means clustering.
• 🔤 Word Embeddings: Captures semantic relationships between words using Word2Vec.
• 🔗 Document Similarity: Identifies related documents using cosine similarity.
• 🏷️ Named Entity Recognition: Extracts and classifies named entities in the text.
• 🕸️ Topic Network Visualization: Visualizes relationships between topics and words.
• 😊 Sentiment Analysis: Analyzes the emotional tone of the text.
• 📚 Text Classification: Automatically categorizes texts using machine learning.
• 📝 Text Summarization: Generates concise summaries of longer texts.
• 🔠 POS Tagging: Assigns parts of speech to words in the text.
• 🌳 Dependency Parsing: Analyzes the grammatical structure of sentences.
• 🧐 Topic Coherence: Evaluates the quality of extracted topics.

🛠️ Requirements

• Python 3.6+
• Required libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • nltk
  • spacy
  • textblob
  • scikit-learn
  • gensim
  • networkx
  • transformers

🚀 Installation

1. Clone this repository:

   git clone https://github.com/DrKenReid/Generalized-Analysis-of-Text-Data.git
   

2. Install required packages:

   pip install -r requirements.txt
   

👨‍💻 Usage

1. Open the notebook in Google Colab or your preferred Jupyter environment.
2. Run all cells in the notebook:
   • In Colab: Runtime -> Run all
   • In Jupyter: Cell -> Run All

📑 Sections

1. Setup: Imports necessary libraries and initializes key components.
2. Data Collection: Fetches the 20 Newsgroups dataset.
3. Dataset Building: Structures the data into a pandas DataFrame.
4. Initial Textual Analysis: Performs basic text statistics.
5. Exploratory Data Analysis: Visualizes key aspects of the data.
6. AI-Enhanced Insights: Applies various NLP techniques for deeper analysis.

📤 Output

The notebook generates various visualizations and outputs, including:

• Word frequency distributions
• Topic models
• Cluster visualizations
• Sentiment analysis results
• Named entity recognition results
• Text summaries

🔧 Customization

You can modify the notebook to use your own dataset by replacing the data collection step with your data loading process.

🤝 Contributing

Contributions, issues, and feature requests are welcome. Feel free to check issues page if you want to contribute.

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgements

• This project uses the 20 Newsgroups dataset for demonstration purposes.
• Special thanks to the developers of the various Python libraries used in this project.

⚖️ Disclaimer

This notebook is for educational and research purposes only. Ensure you have the right to use and analyze any data you input into this notebook.