Trinity at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages using Twitter Dataset

被引：0

作者：

Rathi, Shashank ^{[1
]}

Pande, Siddhesh ^{[1
]}

Atkare, Harshwardhan ^{[1
]}

Tangsali, Rahul ^{[1
]}

Vyawahare, Aditya ^{[1
]}

Kadam, Dipali ^{[1
]}

机构：

[1] PICT, Pune, Maharashtra, India

来源：

17TH INTERNATIONAL WORKSHOP ON SEMANTIC EVALUATION, SEMEVAL-2023 | 2023年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a summary of our findings obtained on sentiment analysis of 3 African languages among the 17 languages mentioned in the shared task. We carried out a sentiment analysis on Hausa, Yoruba, and Swahili. The models used here for the mentioned task were logistic regression, SVM, RandomForest, and mBERT along with a few data-preprocessing and oversampling techniques. The performance of the models used was evaluated by considering weighted average and macro average F1 scores as metrics. The best set of scores obtained on the languages Hausa, Yoruba and Swahili are (76.53, 76.55), (74.83, 73.15) and (57.79, 48.59) respectively.

引用

页码：1161 / 1165

页数：5

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