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Weighted Words Multi-Domain Model for Aspect-Opinion Pairs Extraction | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 خرداد 1404 | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2025.11741.832 | ||
| نویسندگان | ||
| Atefeh Mohammadi؛ Mohammad Reza Pajoohan* ؛ Ali Mohammad Zareh Bidoki | ||
| Department of Computer Engineering, Yazd University, Yazd, Iran. | ||
| تاریخ دریافت: 01 اسفند 1403، تاریخ بازنگری: 27 اردیبهشت 1404، تاریخ پذیرش: 16 خرداد 1404 | ||
| چکیده | ||
| Background and Objectives: In Natural Language Processing (NLP), sentiment analysis is crucial for understanding and extracting aspects and opinions expressed in textual data. Recent methods have emphasized determining polarity in multi-domain sentiment analysis while giving less attention to aspect and opinion extraction. Furthermore, the terms that convey aspects and opinions may have different importance in different domains, and this difference should be considered to enhance the extraction of aspect-opinion pairs. Methods: To address these challenges, we propose a Weighted Words Multi-Domain (WWMD) model for aspect-opinion pairs extraction, consisting of a self-attention mechanism and a dense network. The self-attention mechanism extracts each word's importance according to the sentence's overall meaning. The dense network is used for domain prediction. It assigns greater weight to words relevant to each domain, which leads to considering the different significance of terms across various contexts. Adding an attention mechanism to the domain module allows for a clearer understanding of different aspects and opinions across various domains. We utilize a two-channel approach, one channel extracts aspects and opinions, while the other extracts the relationships between them. The weighted words extracted by our model are simultaneously considered as the input for both channels. Results: Using weighted words specific to each domain, improves the model output. Conclusion: Evaluation results on benchmark datasets demonstrate the superiority of the proposed model compared to state-of-the-art techniques. | ||
| کلیدواژه‌ها | ||
| Multi-domain؛ Opinion entities؛ Sentiment analysis؛ Attention network | ||
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آمار تعداد مشاهده مقاله: 181 |
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