busola-sho/Exploring-Language-Patterns-in-Multilingual-Climate-Messages

Explored a multilingual parallel climate messaging dataset (English, Yoruba, Hausa, Igbo) I co-created. Applied sentence embeddings, K-Means clustering, and dimensionality reduction (PCA, t-SNE) to analyze semantic patterns across languages families. # Exploring Language Patterns in Multilingual Climate Messages ## Overview During the LUNE-TWO Fellowship, I co-created a multilingual dataset of climate messages in English, Yoruba, Hausa, and Igbo alongside 2 linguists. I wanted to see what more could be learned from it, so I ran an experiment: what happens when we cluster these messages using embeddings? This project is my exploration of that question. ## What I Did - Cleaned and tokenised the climate messages. - Used XLM-Roberta to generate sentence embeddings. - Applied `K-Means clustering` and tested cluster quality. - Visualised patterns with `PCA` and `t-SNE`. ## What I Found - Yoruba and Igbo messages often clustered together, which makes sense given their shared Niger–Congo roots. - Hausa, from a different language family (Afro-Asiatic), formed its own group. - The clustering quality was solid (Silhouette Scores: Yoruba 0.605, Hausa 0.579, Igbo 0.618). - Visual plots gave a clear picture of these language relationships. ## Why It Matters This exercise shows how AI/ML techniques can reveal structure in underrepresented languages and hints at applications like: - Better multilingual climate communication. - Tools for translation, retrieval, or summarisation in African contexts. - Making crucial information more accessible across language barriers. ## Next Steps - Try other models like AfriBERTa and mBERT. - Expand to more African languages. - Test downstream tasks such as classification or topic modelling.