💔 The Anatomy of a Modern Breakup

A Multi-Dataset Sentiment & Engagement Analytics Pipeline

Course: Data Analytics — DS26
Student: Okah Ahone Ebwekoh
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

data-analytics-breakup-project/
│
├── datasets/
│ ├── raw/ → Source datasets (not tracked)
│ └── processed/ → Processed dataset (not tracked)
│
├── notebooks/
│ └── DA_jupyter notebook.ipynb
│
├── dashboards/
│ └── dashboard.py → Interactive Streamlit dashboard
│
├── diagrams/ → EDA, sentiment, topic & model visualizations
│
├── reports/ → Project reports and conference paper
│
├── presentations/
│ └── Data analytics final presentation.pptx
│
├── documentation/
│ └── tools_used.docx
│
└── references/
└── citation_list.bib

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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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