💔 The Anatomy of a Modern Breakup

A Multi-Dataset Sentiment & Engagement Analytics Pipeline

Course: Data Analytics — DS26
Student: Okah Ahone Ebwekoh | Student ID: 80770239
Supervisor: Prof. Nor Azizah Hitam
University: University of Europe for Applied Sciences
Submitted: July 2026

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📌 Project Overview

This project analyzes over 2,800 Reddit posts from r/breakups and r/relationship_advice to understand what drives community engagement in online relationship support communities. Using sentiment analysis, topic clustering, and supervised machine learning, the project tests the core hypothesis that structural factors — not emotional content — are the primary drivers of post visibility.

Key finding: Emotional sentiment has near-zero correlation with engagement. Comment count and post timing are the strongest predictors of whether a post goes viral — not how emotional the writing is. We call this the Sentiment Paradox.

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📁 Project Structure

Final_Project_Submission/
│
├── Final_Report_v2.docx              → Full project report (8 sections)
│
├── data/
│   ├── raw/
│   │   ├── relationship_advice.csv   → Original r/relationship_advice dataset
│   │   └── reddit_breakup_dataset_cleaned.csv  → Original r/breakups dataset
│   └── processed/
│       └── merged_clean.csv          → Cleaned, merged dataset (output of notebook)
│
├── notebooks/
│   └── main_analysis.ipynb           → Full analytical pipeline
│
├── dashboards/
│   └── dashboard.py                  → Interactive Streamlit dashboard
│
├── diagrams/
│   ├── eda_overview.png              → EDA charts (score, length, engagement, hour)
│   ├── sentiment_analysis.png        → Sentiment distribution and boxplots
│   ├── wordclouds.png                → Word clouds (positive vs negative)
│   ├── correlation_heatmap.png       → Feature correlation matrix
│   ├── pmf_cdf.png                   → PMF and CDF of post score
│   ├── topic_distribution.png        → LDA topic clustering results
│   ├── confusion_matrices.png        → Baseline model confusion matrices
│   ├── confusion_matrices_improved.png → Improved model confusion matrices
│   ├── feature_importance.png        → RF importance & LR coefficients
│   ├── model_comparison.png          → Baseline model metrics comparison
│   └── improvement_comparison.png    → Before vs after recall improvement
│
├── documentation/
│   └── README.md                     → This file
│
└── references/
    └── citation_list.bib             → APA references

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📊 Datasets

Dataset	Source	Rows	Columns
relationship_advice.csv	Kaggle	1,885	8
reddit_breakup_dataset_cleaned.csv	Kaggle	948	10
Combined (after cleaning)	—	2,825	12

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🛠️ Tools & Technologies

Category	Tools
Language	Python 3
Data Processing	Pandas, NumPy
Text Cleaning	re (regex)
Sentiment Analysis	VADER (vaderSentiment)
Topic Modeling	Scikit-learn (LDA, CountVectorizer)
Machine Learning	Scikit-learn (Logistic Regression, Decision Tree, Random Forest)
Class Imbalance	imbalanced-learn (SMOTE)
Visualization	Matplotlib, Seaborn, WordCloud
Dashboard	Streamlit
Environment	Jupyter Notebook (Anaconda)

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⚙️ How to Run

1. Install dependencies

pip install pandas numpy matplotlib seaborn scikit-learn imbalanced-learn vaderSentiment wordcloud streamlit

2. Run the Jupyter Notebook

Open Anaconda Navigator → Launch Jupyter Notebook → Open notebooks/main_analysis.ipynb → Run All Cells

Make sure the datasets are in data/raw/ before running.

3. Run the Streamlit Dashboard

cd "path/to/Final_Project_Submission"
streamlit run dashboards/dashboard.py

The dashboard will open automatically at http://localhost:8501

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🔑 Key Results

Approach	Best Recall	Best F1
Baseline Models	0.244 (RF)	0.349 (RF)
Class Weighted	0.944 (DT)	0.630 (DT)
SMOTE	0.861 (DT)	0.634 (DT)

5 Topic Clusters identified:

• Heartbreak & Recovery — 30.4%
• Long-term Strain — 21.3%
• Dating & Early Relationships — 17.9%
• Intimacy & Dynamics — 16.2%
• Social & Family Conflict — 14.4%

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📚 References

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. ICWSM.

Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. JMLR, 12, 2825–2830.

Shujon, S. (2025). Reddit break up stories dataset 2023–2025. Kaggle.

The Devastator. (2025). Unveiling relationship dynamics with Reddit. Kaggle.