Team ISCL _ WINTER at S em E val-2023 Task 12: A fri S enti- S em E val: Sentiment Analysis for Low-resource A frican Languages using T witter Dataset (2023)

Abstract: This paper presents a study on the effectiveness of various approaches for addressing the challenge of multilingual sentiment analysis in low-resource African languages. . The approaches evaluated in the study include Support Vector Machines (SVM), translation, and an ensemble of pre-trained multilingual sentimental models methods. The paper provides a detailed analysis of the performance of each approach based on experimental results. In our findings, we suggest that the ensemble method is the most effective with an F1-Score of 0.68 on the final testing. This system ranked 19 out of 33 participants in the competition.

## introduction there is a growing interest in using ai for various natural language processing (nlp) tasks such as sentiment analysis, machine translation, and hateful content detection in african languages. however, most of these languages do not have curated datasets available for developing such ai applications. recently, there have been individual and funded initiatives to create datasets for african languages, but research is needed to determine the suitability of current nlp techniques and the development of techniques to maximize the applications of such datasets. subtask b, task 12 of shared task the @xcite focus on 12 african languages and is designed to strengthen their further development by including languages such as hausa, yoruba, igbo, nigerian pidgin, amharic, tigrinya, oromo, swahili, algerian arabic dialect, kinyarwanda, twi, mozambique portuguese, and moroccan arabic/darija. in this study, the goal is to tackle the problem of sentiment analysis in african languages, which are under-represented in natural language processing (nlp) research. to address this challenge, we employed an ensemble strategy using deep language pre-trained models. these models were individually able to handle the languages present in our dataset, which included 12 african languages. ## system overview descriptions of approaches that were attempted are given here. ## conclusion experimenting with various approaches, we came for several ideas that may impact the performance. in our view, the most challenging fact is a lack of data material. probably the increase in examples and more specific description of parameters to mod- our approach worked best when we used the ensemble approach. through an extensive number of experiments on individual languages, we discovered that our system had better performance than the baseline model provided in the starter kit on languages with arabic script with the best-performing languages such as darija, najia, and hausa, and the worst performing languages were swahili and amharic. the reason for this is unknown. the f1 score of these languages is presented in the attempt to translate the datasets into one language is reasonable, in the output you get a massive amount of data that can be classified efficiently. however, it does not seem to work with african languages. translating a part of the dataset in english we found most of the tweets meaningless as there is a heavy amount of loss of data while translating the tweets, that could be a reason we got especially lower results trying to classify the tweets on translations. nonetheless, we acknowledge that using more sophisticated translation libraries may improve performance. also, we should mention that we did not use any lexicons and cleaning data techniques that might potentially raise the accuracy. the most efficient idea, on our point, was a voting ensemble. meanwhile, it has advantages and disadvantages. with the ensemble, we achieve the highest score on development data (0.72). second, comparing the results, we have noticed that the ensemble predicts significantly better languages with arabic script (an increase of 19% compare to the default model performance shown for the darija language). however, tuning and running those models take a lot of time and computational resources. in the future, it will be a good idea to find the more productive solutions -that we found -for each language and combine them with simple language classifiers.