Deep Ensemble Network for Sentiment Analysis in Bi-lingual Low-resource Languages

Abstract

Sentiment analysis (SA) is the systematic identification, extraction, quantification, and study of affective states and subjective information using natural language processing. It is widely used for analyzing users’ feedback, such as reviews or social posts. Recently, SA has been one of the favorite research domains in NLP due to their wide range of applications, including E-commerce, healthcare, hotel business, and others. Many machine learning and deep learning-based models exist to predict the sentiment of the user’s post. However, the sentiment analysis in low-resource languages such as Kannada, Malayalam, Telugu, and Tamil received less attention due to language complexity and the low availability of required resources. This research fills the gap by proposing an ensemble model for predicting the sentiment of code-mixed Kannada and Malayalam languages. The ensemble of transformer-based models achieved a promising weighted F 1 -score of 0.66 for Kannada code-mixed language. In contrast, the ensemble model of the deep learning framework performed best by achieving a weighted F 1 -score of 0.72 for the Malayalam dataset, outperforming existing research.