https://github.com/suryavamsi-p/conflict-nlp-topic-modeling-sentiment-analysis-using-llms
Extracts insights from 26K+ protest events using BERTopic, Top2Vec, and LLMs for real-world applications like crisis monitoring, policy research, and social unrest analysis.
https://github.com/suryavamsi-p/conflict-nlp-topic-modeling-sentiment-analysis-using-llms
all-mpnet-base-v2 bertopic conflict-data data data-science lda llama2 llms machine-learning mistral-7b nlp nltk protest-analysis pyldavis python3 top2vec topic-modeling transformers visualization
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Extracts insights from 26K+ protest events using BERTopic, Top2Vec, and LLMs for real-world applications like crisis monitoring, policy research, and social unrest analysis.
- Host: GitHub
- URL: https://github.com/suryavamsi-p/conflict-nlp-topic-modeling-sentiment-analysis-using-llms
- Owner: SuryaVamsi-P
- Created: 2025-05-07T03:27:00.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-24T21:37:20.000Z (5 months ago)
- Last Synced: 2025-06-21T10:39:16.762Z (4 months ago)
- Topics: all-mpnet-base-v2, bertopic, conflict-data, data, data-science, lda, llama2, llms, machine-learning, mistral-7b, nlp, nltk, protest-analysis, pyldavis, python3, top2vec, topic-modeling, transformers, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 8.48 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Conflict NLP :- Topic Modeling, Sentiment Analysis, and Classification using LLMs
Extracts insights from 26K+ protest events using BERTopic, Top2Vec, and LLMs for real-world applications like crisis monitoring, policy research, and social unrest analysis.
## Project Overview
This capstone project uses **state-of-the-art NLP techniques** to perform :-
- **Topic Modeling** using BERTopic, Top2Vec, and LLaMA2
- **Sentiment Analysis** to assess public sentiment across global conflicts
- **Text Classification** for conflict categorization
The goal is to transform raw conflict data into **actionable intelligence** for policy makers, researchers, and humanitarian aid groups.
## Key Highlights
- **26,000+ conflict records** from ACLED and Google Trends
- Built **4 different topic modeling pipelines** (LDA, BERTopic, Top2Vec, LLaMA2)
- Boosted coherence score for BERT-based topics
- Visualized topic dominance, distributions & coherence
- Preprocessed multilingual noisy text: stopword removal, tokenization, vectorization
## Techniques Used
| Task | Methodology / Tools |
|-----------------------|------------------------------------------|
| Preprocessing | Python, NLTK, RegEx, Gensim |
| Topic Modeling | BERTopic, LDA, Top2Vec, LLaMA2 |
| Dimensionality Reduction | UMAP, HDBSCAN |
| Sentiment Analysis | Hugging Face Transformers (BERT-based) |
| Classification | Logistic Regression, SVM, RandomForest |
| Visualization | matplotlib, seaborn, pyLDAvis, Plotly |
## Repository Structure
```
├── notebooks/
│ ├── BERTopic_Protest_Classification.ipynb
│ ├── LDA_Protest_Classification.ipynb
│ ├── LLaMA2_TopicModeling_protest_analysis.ipynb
│ └── Top2Vec_TopicModeling_Protest_Analysis.ipynb
│
├── presentations/
│ ├── WorldBank_Final.pptx
│ └── GWU_Capstone_Final.pptx
│
├── data/ # Not uploaded due to size/privacy
├── README.md
```
## Use Cases
- **Crisis Detection**: Detect and visualize emerging unrest topics
- **Policy Research**: Extract protest drivers across countries
- **Social Analytics**: Map sentiment trends over time or region
## How to Run
1. Clone the repo: `git clone https://github.com/your-username/your-repo-name`
2. Install dependencies from `requirements.txt`
3. Run the Jupyter notebooks inside `notebooks/`
## Contact
**Surya Vamsi Patiballa**
Graduate Student, MS in Data Science — George Washington University (GWU)
- Email :- svamsi2002@gmail.com
- LinkedIn :- https://www.linkedin.com/in/surya-patiballa-b724851aa/
- Resume :- https://drive.google.com/file/d/178IYcArC6YYVdJiIwRmJYodzKZ-JXe-D/view?usp=sharing
> _"Transforming data into dialogue. Insights into action."_
## If you found this project insightful, feel free to star it!!!