Skip to content

Latest commit

 

History

History
85 lines (73 loc) · 3.56 KB

README.md

File metadata and controls

85 lines (73 loc) · 3.56 KB
Raw

📊 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.