  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.
