Enhancing Sentiment Analysis in Amharic: Leveraging Transformer-Based Language Model for Low-Resource African Languages

Tasks

Languages

Licenses

https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037

Similar

A Transformer-Based Language Model for Sentiment Classification and Cross-Linguistic Generalization: Empowering Low-Resource African LanguagesEmpowering Sentiment Analysis in African Low-Resource Languages Through Transformer Models and Strategic Language SelectionBhattacharya_Lab at SemEval-2023 Task 12: A Transformer-based Language Model for Sentiment Classification for Low Resource African Languages: Nigerian Pidgin and YorubaImproving transformer model translation for low resource South African languages using BERTIammteo/Sentiment-analysis-model-for-Nigerian-pidgin-Low-resource-language-Enhancing African low-resource languages: Swahili data for language modelling

A Transformer-Based Language Model for Sentiment Classification and Cross-Linguistic Generalization: Empowering Low-Resource African Languages

Empowering Sentiment Analysis in African Low-Resource Languages Through Transformer Models and Strategic Language Selection

Bhattacharya_Lab at SemEval-2023 Task 12: A Transformer-based Language Model for Sentiment Classification for Low Resource African Languages: Nigerian Pidgin and Yoruba

Improving transformer model translation for low resource South African languages using BERT

Iammteo/Sentiment-analysis-model-for-Nigerian-pidgin-Low-resource-language-

This is a machine learning project of a Nigerian Pidgin sentiment analysis model specifically design

Enhancing African low-resource languages: Swahili data for language modelling

Language modelling using neural networks requires adequate data to guarantee quality word representation which is important for natural language processing (NLP) tasks. However, African languages, Swahili in particular, have been disadvantaged and most of them are