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

TL;DR

This paper presents a CNN-based model for aspect and sentiment classification in Hausa movie reviews, creating a new dataset and achieving high accuracy, thus advancing sentiment analysis in underrepresented languages.

Contribution

It introduces a novel CNN with attention mechanisms for Hausa ABSA and provides the first comprehensive Hausa sentiment analysis dataset.

Findings

91% accuracy in aspect term extraction

92% accuracy in sentiment polarity classification

Outperforms traditional models in Hausa ABSA

Abstract

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.