N olly S enti: Leveraging Transfer Learning and Machine Translation for N igerian Movie Sentiment Classification (2023)

Abstract: Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there have been progress in developing labelled corpora for African languages. However, they are often available in a single domain and may not generalize to other domains. In this paper, we focus on the task of sentiment classification for cross-domain adaptation. We create a new dataset, Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian Pidgin, and Yoruba). We provide an extensive empirical evaluation using classical machine learning methods and pre-trained language models. By leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language. Our evaluation shows that transfer from English in the same target domain leads to more than 5% improvement in accuracy compared to transfer from Twitter in the same language. To further mitigate the domain difference, we leverage machine translation from English to other Nigerian languages, which leads to a further improvement of 7% over cross-lingual evaluation. While machine translation to low-resource languages are often of low quality, our analysis shows that sentiment related words are often preserved.

## introduction nigeria is the sixth most populous country in the world foot_0 and the most populous in africa with over 500 languages @xcite . these languages are spoken by millions of speakers, and the four most spoken indigenous languages (hausa, igbo, nigerian-pidgin (naija), and yorùbá) have more than 25 million speakers but they are still under-represented in nlp research @xcite . the development of nlp for nigerian languages and other african languages is often limited by a lack of labelled datasets @xcite . while there have been some progress in recent years @xcite @xcite @xcite , most benchmark datasets for african languages are only available in a single domain, and may not transfer well to other target domains of interest @xcite . one of the most popular nlp tasks is sentiment analysis. in many high-resource languages like english, sentiment analysis datasets are available across several domains like social media posts/tweets @xcite , product reviews @xcite and movie reviews @xcite . however, for nigerian languages, the only available dataset is naijasenti @xcite -a twitter sentiment classification dataset for four most-spoken nigerian languages. it is unclear how it transfers to other domains. in this paper, we focus on the task of sentiment classification for cross-domain adaptation. we create the first sentiment classification dataset for nollywood movie reviews known as nollysenti -a dataset for five widely spoken nigerian languages (english, hausa, igbo, nigerian-pidgin, and yorùbá). nollywood is the home for nigerian movies that depict the nigerian people and reflect the diversities across nigerian cultures. our choice of this domain is because nollywood is the second-largest movie and film industry in the world by annual output 2 , and the availability of nollywood reviews on several online websites. however, most of these online reviews are only in english. to cover more languages, we asked professional translators to translate about 1,000-1,500 reviews from english to four nigerian languages, similar to @xcite . thus, nollysenti is a parallel multilingual sentiment corpus for five nigerian languages that can be used for both sentiment classification and evaluation of machine translation (mt) models in the user-generated texts domainwhich is often scarce for low-resource languages. additionally, we provide several supervised and transfer learning experiments using classical machine learning methods and pre-trained language models. by leveraging transfer learning, we compare the performance of cross-domain adaptation from the twitter domain to the movie domain, and cross-lingual adaptation from english language. our evaluation shows that transfer from english in the same target domain leads to more than 5% improvement in accuracy compared to transfer from the twitter domain in the same target language. to further mitigate the domain difference, we leverage mt from english to other nigerian languages, which leads to a further improvement of 7% over cross-lingual evaluation. while mt to low-resource languages are often of low quality, through human evaluation, we show that most of the translated sentences preserve the sentiment in the original english reviews. for reproducibility, we have released our datasets and code on github foot_2 . ## related work african sentiment datasets there are only a few sentiment classification datasets for african languages such as amharic dataset @xcite , and naijasenti (muhammad et al., 2022)for hausa, igbo, nigerian-pidgin, and yorùbá. recently, @xcite expanded the sentiment classification dataset to 14 african languages. however, all these datasets belong to the social media or twitter domain. in this work, we create a new dataset for the movie domain based on human translation from english to nigerian languages, similar to the nusax parallel sentiment corpus for 10 indonesia languages @xcite . ## experimental setup data split ## conclusion in this paper, we focus on the task of sentiment classification for cross-domain adaptation. we developed a new dataset, nollysenti for five nigerian languages. our results show the potential of both transfer learning and mt for developing sentiment classification models for low-resource languages. as a future work, we would like to extend the creation of movie sentiment corpus to more african languages.