Thesis Python Scripts – Cagri Demirci (2025)

This repository contains Python scripts developed for the MSc Thesis project by Cagri Demirci, focusing on code-mixing analysis, language interaction, and digital communication analytics.
Each file represents an independent analytical module that can be executed individually or imported as part of a larger workflow.

📘 Project Overview

These scripts were created to support linguistic and computational research on English lexical borrowings in Turkish social media texts.
The analyses focus on code-mixing ratios, English root extraction, engagement measurement, and general exploratory data analysis (EDA).

📂 File Descriptions

code_mixing_ratio.py

Calculates the Code-Mixing Ratio (CMR) by identifying English tokens within multilingual text data.

• Detects English-origin words in mixed-language texts.
• Returns a numeric ratio (English words / total words).
• Compatible with both single-text and pandas Series input.

english_root_extraction.py

Extracts English roots from Turkish-English hybrid words such as like'lamak or save'ledim.
Applies optional stemming (via NLTK) for root normalization.

• Regex-based detection of English substrings.
• Optional stemming for root-level analysis.
• Returns token lists and frequency counts.

youtube_engagement_rate.py

Computes YouTube engagement rate using likes, comments, and views.

Engagement Rate = ((Likes + Comments) / Views) * 100

• Safe division (avoids division by zero).
• Vectorized computation for pandas DataFrames.
• Outputs engagement rate per video.

exploratory_data_analysis.py

Performs basic exploratory data analysis (EDA) on text datasets.

• Supports CSV and JSON input.
• Provides descriptive statistics and optional visualization.
• Exportable as summary tables.

⚙️ How to Run

Clone this repository and navigate into the folder:

git clone https://github.com/yourusername/thesis_python_scripts.git
cd thesis_python_scripts

Run each script individually:

python code_mixing_ratio.py
python english_root_extraction.py
python youtube_engagement_rate.py
python exploratory_data_analysis.py data.csv

Or import functions directly in a Jupyter Notebook or Python environment.

🔐 Important Note on API Keys

All sensitive credentials have been removed.
If an API call is needed, replace the placeholder line:

API_KEY = "YOUR_API_KEY"

with your own valid key before execution.

🧠 Citation

Demirci, C. (2025). Code-Mixing in Digital Communication: Data-Driven Analysis of the Use of English in Turkish Social Media Texts.
MSc Thesis, Erciyes University.

📄 License

These scripts are provided for academic and research purposes under an open educational license.
You may reuse or adapt them with proper attribution to the author.

Author: Cagri Demirci
Year: 2025
Email: cagridemirci1@gmail.com
Location: Kayseri, Türkiye