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.

这篇论文介绍了一个针对豪萨语电影评论的方面和极性分类的基于深度卷积神经网络（CNN）的模型，该模型是针对理解文本中的情感细微差别而进行的，特别是针对不同语言和文化。作者创建了一个包含人工注释的综合豪萨语 ABSA 数据集，填补了资源可用性方面的重要空白。通过将 CNN 与注意机制相结合进行方面词预测，该模型利用上下文信息和情感极性，准确率达到了 91% 的方面词提取和 92% 的极性分类，优于传统的机器模型，为特定方面和情感提供了深入的洞察力。这项研究推动了 ABSA 研究，特别是在不常见的语言方面，并对跨文化语言研究产生了影响。