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Statistical Models for Natural Language Processing and Topic Modelling using R

Description

This repository contains the code and documentation for my thesis titled "Statistical Models for Natural Language Processing and Topic Modelling using R." written to obtain my second master's degree in Athens University of Business and Economics). The thesis explores various statistical models and techniques used in the field of natural language processing (NLP) and focuses on their application in topic modeling. The thesis has been created as part of obtaining my second MSc in Applied Statistics in AUEB.

Contents

  1. Data 📊: This folder contains the data scraped and saved for use in fitting the models discussed in the thesis.

  2. Extractor 📝: The R scripts used to scrape the data, which are saved in the Data folder, are stored here.

  3. Models 🧠: This folder contains R scripts where the models discussed in the thesis are fitted.

  4. Thesis_WIP.pdf 📄: The current version of the thesis, a work in progress.

Usage ⚙️

  • Data: The data in this folder can be used to replicate the experiments and results discussed in the thesis.

  • Extractor: These scripts can be used to scrape data from relevant sources for future research or to update the dataset.

  • Models: The R scripts in this folder can be utilized to replicate the model fitting process and analyze different statistical models for natural language processing and topic modeling.

How to Cite 🤓

If you use any part of this work in your research or publication, please cite it as:

Themis Kavour. "Statistical Models for Natural Language Processing and Topic Modelling using R." 2024. Available at: AUEB-Thesis.

Contact 📫

For any inquiries or feedback regarding this work, please contact Themis Kavour at here.

Acknowledgements 🙏🏼

I extend my heartfelt appreciation to Professor Panagiotis Papastamoulis for his exceptional guidance, unwavering support, and profound encouragement throughout the development of this thesis. His expertise and insightful feedback have not only shaped the direction of this research project but have also influenced my academic progression to this day. I am deeply grateful for his dedication and mentorship, which have enriched my academic journey and played a pivotal role in the completion of this work.

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All the computations that one need to replicate the results described in my thesis.

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