A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915602928500736 |
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| author | Ibrahim, Umar Zandam, Abubakar Yakubu Adam, Fatima Muhammad Musa, Aminu |
| author_facet | Ibrahim, Umar Zandam, Abubakar Yakubu Adam, Fatima Muhammad Musa, Aminu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19575 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews Ibrahim, Umar Zandam, Abubakar Yakubu Adam, Fatima Muhammad Musa, Aminu Computation and Language Artificial Intelligence 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. |
| title | A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.19575 |