Building lexicon-based sentiment analysis model for low-resource languages

Natural Language Processing (NLP) has transformed machine translation, sentiment analysis, information retrieval, and conversation systems. NLP applications rely on complete linguistic resources, which might be difficult for low-resource languages. NLP solutions for every language require a language-specific dataset. Dataset in a language is essential for NLP solution creation. Over 7000 languages are spoken worldwide. Only around 20 languages have text corpora for NLP applications. English has the most datasets, then Chinese and Spanish. Japanese has several Western European language datasets. For an accurate NLP system, most Asian and African languages lack training datasets. To address this challenge, we propose a methodology for building a lexicon-based sentiment analysis model for languages with limited resources. The Hausa language was used as training and evaluation language. The methodology combines lexicon creation; augmentation, annotation, and fine-tuning model, and has been tested on a corpus of Hausa tweets achieving an accuracy of 98 %. The results suggest that our proposed model is a promising tool for sentiment analysis in a variety of applications, such as social media monitoring, customer service, and market research. Our methodology can be used for any low-resource language. The outline of the work done in this paper can be shown as follows: • We propose a methodology for building a lexicon-based sentiment analysis model for languages with limited resources, using the Hausa language as a case study. • The methodology combines lexicon creation, augmentation, annotation, and fine-tuning model, and achieves an accuracy of 98 % on a corpus of Hausa tweets. • The results suggest that the proposed model is a promising tool for sentiment analysis in a variety of applications for low-resource languages.