A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews
<|reference_start|>A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews: Aspect-based Sentiment Analysis (ABSA) is crucial for understanding sentiment nuances in text, especially across diverse languages and cultures. This paper introduces a novel Deep Convolutional Neural Network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, an underrepresented language in sentiment analysis research. A comprehensive Hausa ABSA dataset is created, filling a significant gap in resource availability. The dataset, preprocessed using sci-kit-learn for TF-IDF transformation, includes manually annotated aspect-level feature ontology words and sentiment polarity assignments. The proposed model combines CNNs with attention mechanisms for aspect-word prediction, leveraging contextual information and sentiment polarities. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model outperforms traditional machine models, offering insights into specific aspects and sentiments. This study advances ABSA research, particularly in underrepresented languages, with implications for cross-cultural linguistic research.<|reference_end|>
@article{ibrahim2024a,
    title={A Deep Convolutional Neural Network-based Model for Aspect and Polarity
  Classification in Hausa Movie Reviews},
    author={Umar Ibrahim, Abubakar Yakubu Zandam, Fatima Muhammad Adam, Aminu Musa},
    journal={arXiv preprint arXiv:2405.19575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19575},
    primaryClass={cs.CL cs.AI}
}