Sentiment Polarity Analysis of Amharic Climate Change Discourse Using Large Language Models

Gebregziabihier Nigusie, Neima Mossa, Tesfa Tegegne
DLI 2025 Research Track, PMLR 302:1-10, 2026.

Abstract

Climate change refers to variations in temperature and weather conditions due to various climate-related factors on earth. These factors vary across regions, and people’s perceptions of climate change. Analyzing public opinion on climate change at a regional level is crucial for developing targeted solutions. However, manually analyzing large volumes of data is challenging for informed dissension. Applying emerging pre-trained Large Language Models offers a promising solution for efficiently analyzing large datasets and understanding public perspectives on climate change. Amharic is one of the widely spoken African languages. Many speakers of the language are actively discussing and expressing their opinions on various topics, including climate change, on social media. Given the increasing discussions about climate change, this study focuses on the sentiment analysis of Amharic climate texts. We collected 6013 sentences from social media and news sources. The data is annotated manually by native speakers to its target polarity. We conducted experiments using the LLM that supports African languages during pre-training. In this study, MultilingualBert and AfriBERTa models were employed with hyperparameter tuning to perform sentiment polarity analysis on Amharic climate text. The experimental results shows that MultilingualBert outperforms AfriBERTa, achieving an accuracy of 69%. This performance is attributed to MultilingualBert’s enhanced capability to capture token-level semantics by giving a variety of attention across tokens, thereby improving its contextual understanding in downstream sentiment classification tasks. Keywords: Climate NLP, Sentiment, LLM, mBERT, AfriBERTa, Climate Sentiment.

Cite this Paper

BibTeX
@InProceedings{pmlr-v302-nigusie26a, title = {Sentiment Polarity Analysis of Amharic Climate Change Discourse Using Large Language Models}, author = {Nigusie, Gebregziabihier and Mossa, Neima and Tegegne, Tesfa}, booktitle = {DLI 2025 Research Track}, pages = {1--10}, year = {2026}, editor = {Haddad, Hatem and Kahira, Albert Njoroge and Bourhim, Sofia and Olatunji, Iyiola Emmanuel and Makhafola, Lesego and Mwase, Christine}, volume = {302}, series = {Proceedings of Machine Learning Research}, month = {17--22 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v302/main/assets/nigusie26a/nigusie26a.pdf}, url = {https://proceedings.mlr.press/v302/nigusie26a.html}, abstract = {Climate change refers to variations in temperature and weather conditions due to various climate-related factors on earth. These factors vary across regions, and people’s perceptions of climate change. Analyzing public opinion on climate change at a regional level is crucial for developing targeted solutions. However, manually analyzing large volumes of data is challenging for informed dissension. Applying emerging pre-trained Large Language Models offers a promising solution for efficiently analyzing large datasets and understanding public perspectives on climate change. Amharic is one of the widely spoken African languages. Many speakers of the language are actively discussing and expressing their opinions on various topics, including climate change, on social media. Given the increasing discussions about climate change, this study focuses on the sentiment analysis of Amharic climate texts. We collected 6013 sentences from social media and news sources. The data is annotated manually by native speakers to its target polarity. We conducted experiments using the LLM that supports African languages during pre-training. In this study, MultilingualBert and AfriBERTa models were employed with hyperparameter tuning to perform sentiment polarity analysis on Amharic climate text. The experimental results shows that MultilingualBert outperforms AfriBERTa, achieving an accuracy of 69%. This performance is attributed to MultilingualBert’s enhanced capability to capture token-level semantics by giving a variety of attention across tokens, thereby improving its contextual understanding in downstream sentiment classification tasks. Keywords: Climate NLP, Sentiment, LLM, mBERT, AfriBERTa, Climate Sentiment.} }
Endnote
%0 Conference Paper %T Sentiment Polarity Analysis of Amharic Climate Change Discourse Using Large Language Models %A Gebregziabihier Nigusie %A Neima Mossa %A Tesfa Tegegne %B DLI 2025 Research Track %C Proceedings of Machine Learning Research %D 2026 %E Hatem Haddad %E Albert Njoroge Kahira %E Sofia Bourhim %E Iyiola Emmanuel Olatunji %E Lesego Makhafola %E Christine Mwase %F pmlr-v302-nigusie26a %I PMLR %P 1--10 %U https://proceedings.mlr.press/v302/nigusie26a.html %V 302 %X Climate change refers to variations in temperature and weather conditions due to various climate-related factors on earth. These factors vary across regions, and people’s perceptions of climate change. Analyzing public opinion on climate change at a regional level is crucial for developing targeted solutions. However, manually analyzing large volumes of data is challenging for informed dissension. Applying emerging pre-trained Large Language Models offers a promising solution for efficiently analyzing large datasets and understanding public perspectives on climate change. Amharic is one of the widely spoken African languages. Many speakers of the language are actively discussing and expressing their opinions on various topics, including climate change, on social media. Given the increasing discussions about climate change, this study focuses on the sentiment analysis of Amharic climate texts. We collected 6013 sentences from social media and news sources. The data is annotated manually by native speakers to its target polarity. We conducted experiments using the LLM that supports African languages during pre-training. In this study, MultilingualBert and AfriBERTa models were employed with hyperparameter tuning to perform sentiment polarity analysis on Amharic climate text. The experimental results shows that MultilingualBert outperforms AfriBERTa, achieving an accuracy of 69%. This performance is attributed to MultilingualBert’s enhanced capability to capture token-level semantics by giving a variety of attention across tokens, thereby improving its contextual understanding in downstream sentiment classification tasks. Keywords: Climate NLP, Sentiment, LLM, mBERT, AfriBERTa, Climate Sentiment.
APA
Nigusie, G., Mossa, N. & Tegegne, T.. (2026). Sentiment Polarity Analysis of Amharic Climate Change Discourse Using Large Language Models. DLI 2025 Research Track, in Proceedings of Machine Learning Research 302:1-10 Available from https://proceedings.mlr.press/v302/nigusie26a.html.

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