Abstract
With the growth of social networks, an increasing number of publicly available opinions are posted on them. Sentiment analysis, especially aspect-level sentiment analysis (ALSA), of these opinions has emerged and is a concern for many researchers. ALSA aims to gather, evaluate, and aggregate sentiments regarding the aspects of a topic of concern. Previous research has demonstrated that deep learning and graph convolutional network (GCN) methods can effectively improve the performance of ALSA methods. However, further investigation is required, especially when comparing the performance of deep learning-based and GCN-based ALSA methods on balanced and unbalanced datasets. In this study, we aimed to investigate two hypotheses: (i) the effectiveness of ALSA methods can be improved by deep learning and GCN techniques on balanced and unbalanced datasets, especially GCNs, over BERT representations, and (ii) the balanced data slightly affect the accuracy and \(F_1\) score of the deep learning and GCN-based ALSA methods. To implement this study, we first constructed balanced Laptop, Restaurant, and MAMS datasets based on their original unbalanced datasets; then, we experimented with 17 prepared methods on six unbalanced and balanced datasets; finally, we evaluated, discussed, and concluded the two hypotheses.
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Acknowledgment
This research was supported by the 2020 Yeungnam University Research Grant. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2023R1A2C1008134).
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Phan, H.T., Nguyen, N.T., Seo, YS., Hwang, D. (2023). Deep-Learning- and GCN-Based Aspect-Level Sentiment Analysis Methods on Balanced and Unbalanced Datasets. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13996. Springer, Singapore. https://doi.org/10.1007/978-981-99-5837-5_12
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