Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language

Abstract

This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on Twitter for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, BERT (Bidirectional Encoder Representations from Transformers) and XLNet, along with topic modeling algorithms; namely, LDA (Latent Dirichlet Allocation) and NMF (Non-negative Matrix Factorization), for aspect-based sentiment analysis of online Arabic text. In addition, Bi-LSTM (Bidirectional Long Short Term Memory) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.