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A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews

Domain:

natural language processing

Record type:

paperdatasetmodel
Creator:
IbrZanAdaMus
Host:avatar
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. To be published in the proceedings of ICCAIT 2023

Visit

arxiv.org

Tasks

sentiment analysistext classification

Languages

Hausa

Tags

Computation and LanguageArtificial Intelligence

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