Citation
Muhammad Azman Busst, Mikail and Sonai Muthu Anbananthen, Kalaiarasi and Kannan, Subarmaniam (2024) Aspect-Level Sentiment Analysis through Aspect-Oriented Features. HighTech and Innovation Journal, 5 (1). pp. 109-128. ISSN 2723-9535
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Abstract
Aspect-level sentiment analysis is essential for businesses to comprehend sentiment polarities associated with various aspects within unstructured texts. Although several solutions have been proposed in recent studies in sentiment analysis, a few challenges persist. A significant challenge is the presence of multiple aspects within a single written text, each conveying its own sentiments. Besides this, the exploration of ensemble learning in the existing literature is limited. Therefore, this study proposes a novel aspect-level sentiment analysis solution that utilizes an ensemble of Bidirectional Long Short-Term Memory (BiLSTM) models. This innovative solution extracts aspects and sentiments and incorporates a rule-based algorithm to combine accurate sets of aspect and sentiment features. Experimental analysis demonstrates the effectiveness of the proposed methodology in accurately extracting aspect-level sentiment features from input texts. The proposed solution was able to obtain an F1 score of 92.98% on the SemEval-2014 Restaurant dataset when provided with the correct set of aspect-level sentiment features and an F1 score of 95.54% on the SemEval-2016 Laptop dataset when provided with the aspect-level sentiment features generated by the aspect-sentiment mapper algorithm.
Item Type: | Article |
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Uncontrolled Keywords: | Aspect-Level Sentiment Analysis; Ensemble Model; Deep Learning. |
Subjects: | Q Science > QA Mathematics > QA801-939 Analytic mechanics |
Divisions: | Faculty of Information Science and Technology (FIST) |
Depositing User: | Ms Nurul Iqtiani Ahmad |
Date Deposited: | 02 Apr 2024 04:23 |
Last Modified: | 02 Apr 2024 04:23 |
URII: | http://shdl.mmu.edu.my/id/eprint/12273 |
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