Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text

Abstract

Aspect extraction poses a significant challenge in Natural Language Processing (NLP). Despite significant research efforts, extracting explicit and implicit aspects from online text data remains an ongoing challenge. Enhancing the accuracy and effectiveness of aspect extraction is an important area for improvement. This research introduces Ensemble BiLSTM, a novel approach to aspect extraction that addresses these challenges. Ensemble BiLSTM leverages the combination of the syntactic, semantic, and contextual properties of unstructured texts that are present in BERT word embeddings, along with their sequential properties that are captured using an ensemble of Bidirectional Long Short-Term Memory (BiLSTM) models. The proposed Ensemble BiLSTM model was evaluated extensively using the SemEval-2014 Restaurant, SemEval-2015 Restaurant, SemEval-2016 Laptop, and Financial Opinion Mining and Question Answering (FiQA) datasets. The experimental results demonstrate its efficacy in extracting aspects from text, achieving 91.28%, 87.39%, 95.85%, and 94.59% accuracy on the respective datasets. These promising results highlight the effectiveness of the ensemble approach and the incorporation of sequential models combined with BERT embeddings. The contributions of this research lie in the aspect category features extracted by the proposed Ensemble BiLSTM model, which can be expanded upon to generate accurate aspect-level sentiment features.

Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language

“…Various approaches, including Recurrent Neural Networks (RNNs), CNNs, and Transformer models, have exhibited positive results in this domain. RNNs, particularly LSTM networks, have become increasingly popular due to their effectiveness in processing sequential data [29]. In terms of transformer models, the transfer learning ability improved the accuracy of many NLP applications [30,31].…”