PingAnLifeInsurance at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages with Multi-Model Fusion

This paper describes our system used in the SemEval-2023 Task12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset. 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. Task A is a monolingual sentiment classification which covered 12 African languages. Task B is a multilingual sentiment classification which combined training data from Task A (12 African languages). Task C is a zero-shot sentiment classification. We utilized various strategies, including monolingual training, multilingual mixed training, and translation technology, and proposed a weighted voting method that combined the results of different strategies. Substantially, in the monolingual subtask, our system achieved Top-1 in two languages (Yoruba and Twi) and Top-2 in four languages (Nigerian Pidgin, Algerian Arabic, and Swahili, Multilingual). In the multilingual subtask, Our system achived Top-2 in publish leaderBoard.