Counter-Strike Reddit Sentiment
& Topic Analysis
(2012–2025)
This project applies Natural Language Processing (NLP) techniques to analyze Reddit comments related to Counter-Strike from 2012 to 2025.
Goals:
Understand player sentiment trends over time.
Identify major controversies and discussion hotspots.
Uncover underlying topics in community discussions.
Project Overview
Tools and Technologies
| Category | Tools |
|---|---|
| Programming | Python |
| Data Processing | Pandas, NumPy |
| Text Analysis | NLTK, VADER, SpaCy |
| Topic Modeling | Gensim (LDA), pyLDAvis |
| Visualization | Matplotlib, WordCloud, pyLDAvis |
| Deployment | Jupyter Notebook, GitHub |
Sentiment Trend Analysis
Tracked the evolution of community sentiment and detected major negative sentiment spikes corresponding to known events (e.g., cheating scandals, VAC bans).
Negative Word Cloud
Extracted frequent terms from negative comments, highlighting dissatisfaction points like cheating, server issues, and gameplay frustrations.
Controversial Posts Identification
Analyzed posts attracting the highest number of negative comments, uncovering community pain points.
Monthly Sentiment Trends
Built a timeline of negative sentiment fluctuations from 2019 to 2025.
LDA Topic Modeling
Applied topic modeling to reveal hidden thematic structures in negative comments, clustering discussions into gameplay experiences, technical issues, community interactions, and cheating concerns.
Key Analyses
Selected Visualizations
Cheating remains the most critical issue discussed negatively across all years.
Community sentiment showed major spikes in negativity during high-profile VAC ban waves and controversial updates.
Player discussions evolved from gameplay frustrations to more community-centered issues over time.
Key Insights
Train Accurate Sentiment Analysis Model: Move beyond lexicon-based methods like VADER by fine-tuning BERT for deeper understanding of sarcasm and complex emotions in gaming communities.
Refine Data Collection Methods: Instead of broad web crawling, selectively retrieve controversial Reddit threads to enhance sentiment analysis.
Track Sentiment Evolution: Monitor community sentiment after major CS2 updates to gain insights into player adaptation and satisfaction.
Correlate Sentiment Trends: Link sentiment fluctuations to specific patches, tournaments, or events for enriched context.
Explore Dynamic Topic Modeling: Use dynamic topic modeling to visualize shifts in community discussions over time, highlighting evolving player concerns.