A Mathematical Tribute to The Ramayan
- Jignyasa is a statistical dive into the world of The Valmiki Ramayan - its sentiments, graphs and communities - built with Python and deployed on Streamlit.
- Find the analysis and insights in this article.
Books are rich sources of textual data, astounding in their complexity. They might be infinite storehouses of wisdom and knowledge, informing their reader of the universe and beyond, or might spin tales of far-away lands and times long past, crafting entire worlds with nothing but words. Leveraging statistics, some basic math and a couple of algorithms can provide a different perspective that can go a long way in offering a glimpse into these worlds.
- Pre-Processing the Data
- Exploratory Analysis with NLTK:
- Wordcloud
- Concordance
- Dispersion Plots
- Plotting Character Presence
- Character Networks
- Directed and Undirected Graphs
- Sentiment Analysis
- Polarities with VADER
- NetworkX graph construction
- Visualisation with pyVis
- Centralities:
- Degree
- Betweenness
- K-Cores
- Deployment with streamlit
- For a quick view of the project, find the Streamlit Deployment here.
- To run the app on your local server, get started like so.
- Clone the project
git clone https://github.com/su-mana-s/Ramayanam.git
- Open command line and navigate to the project folder streamlit source folder - Ramayanam/src/
cd Ramayanam/src
- Run the app
streamlit run Jignyasa.py
