NLP - LISAC at S em E val-2023 Task 12: Sentiment Analysis for Tweets expressed in A frican languages via Transformer-based Models (2023)

Abstract: This paper presents our systems and findings for SemEval-2023 Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages. The main objective of this task was to determine the polarity of a tweet (positive, negative, or neutral). Our submitted models (highest rank is 1 and lowest rank is 21 depending on the target Track) consist of various Transformer-based approaches.

## introduction sentiment analysis is a branch of natural language processing that involves the use of computational techniques to identify and extract subjective information from textual data. it involves the classification of text based on the writer's attitude, emotions, and opinions toward a particular subject or topic. in recent years, there has been a growing interest in sentiment analysis for african languages. this is because african languages are some of the most widely spoken languages in the world, yet they are often underrepresented in natural language processing research. however, sentiment analysis for african languages presents unique challenges due to the linguistic and cultural diversity of the continent. one of the main challenges of sentiment analysis for african languages is the lack of annotated data. annotated data is essential for training machine learning models for sentiment analysis, but there is a limited amount of annotated data available for many african languages. additionally, african languages often have complex grammatical structures and a wide range of dialects, making it difficult to develop effective sentiment analysis models. despite these challenges, there have been some recent advances in sentiment analysis for african languages. researchers have used transfer learning and cross-lingual models to develop sentiment analysis models for african languages, which can leverage existing data from other languages to improve performance. there have also been efforts to create annotated datasets specifically for african languages. this paper presents our findings on semeval-2023 task 12: afrisenti-semeval: sentiment analysis for low-resource african languages @xcite . our method consists of finetuning various transformer-based @xcite models. the rest of the paper is structured in the following manner: section 2 provides the main objective of each sub-task. section 3 describes the employed models. section 4 details the experiments. and finally, section 5 concludes this paper. ## task description the afrisenti-semeval shared task 12 is based on a collection of twitter datasets in 14 african languages for sentiment classification. it consists of three sub-tasks: there are 12 tracks in this sub-task, each for a different language: track 1 for hausa, track 2 for yoruba, track 3 for igbo, track 4 for nigerian pidgin, track 5 for amharic, track 6 for algerian arabic, track 7 for moroccan arabic/darija, track 8 for swahili, track 9 for kinyarwanda, track 10 for twi, track 11 for mozambican portuguese, and track 12 for xitsonga (mozambique dialect). ## system description to tackle afrisenti-semeval shared task 12, several transformer-based models have been used depending on each track: ## experimental results the experiments have been conducted in google colab environment 9 , the following libraries: transformers -hugging face 10 (wolf et al., 2020), and keras 11 were used to fine-tune and to assess the performance of our models. ## conclusion in this paper, we described our approach for tackling semeval 2023 task 12: afrisenti-semeval: sentiment analysis for low-resource african languages @xcite . our proposed approach consisted of various transformer-based models. we secured 1st position in track 6: algerian arabic, and 3rd position in track 7: moroccan arabic/darija. future studies will focus on improving the obtained results by (1) incorporating external resources including sentiment corpus @xcite @xcite and lexicons for african languages, and (2) fine-tuning other transformerbased models @xcite pretrained on african languages foot_10 @xcite .