Nadeem Asma, Missen Malik Muhammad Saad, Reshan Mana Saleh Al, Memon Muhammad Ali, Asiri Yousef, Nizamani Muhammad Ali, Alsulami Mohammad, Shaikh Asadullah
Information Technology, Islamia University, Bahawalpur, Punjab, Pakistan.
Information System, Najran University, Najran Province, Saudi Arabia.
PeerJ Comput Sci. 2025 Jan 13;11:e2635. doi: 10.7717/peerj-cs.2635. eCollection 2025.
In the ever-expanding digital landscape, the abundance of user-generated content on consumer platforms such as Booking and TripAdvisor offers a rich source of information for both travellers and hoteliers. Sentiment analysis, a fundamental research task of natural language processing (NLP) is used for mining sentiments and opinions within this vast reservoir of text reviews. A more specific type of sentiment analysis, ., aspect-based sentiment analysis (ABSA), is used when processing customer reviews is required. In ABSA, we aim to capture aspect-level sentiments and intricate relationships between various aspects within reviews. This article proposes a novel approach to ABSA by introducing a novel technique of word sense disambiguation (WSD) and integrating it with the Transformer architecture bidirectional encoder representations from Transformers (BERT) and graph convolutional networks (GCNs). The proposed approach resolves the intriguing ambiguities of the words and represents the review data as a complex graph structure, facilitating the modeling of intricate relationships between different aspects. The combination of bidirectional long short-term memory (BiLSTM) and GCN proves effective in capturing inter-dependencies among various aspects, providing a nuanced understanding of customer sentiments. The experiments are conducted on the RABSA dataset (an enhanced and richer hotel review data collection), and results demonstrate that our approach outperforms previous baselines, showcasing the effectiveness of integrating WSD in ABSA. Furthermore, an ablation study confirms the significant contribution of the WSD module to the overall performance. Moreover, we explore different similarity measures and find that cosine similarity yields the best results when identifying the real sense of a word in a given sentence using WordNet. The findings of our work and future work related to our work create lots of interest for people in the tourism and hospitality industry. This research gives another boost to the concept of the potential of NLP techniques in sentiment analysis. It emphasizes that if we combine the potential of NLP techniques along with state-of-the-art machine learning frameworks, we can shape the future of this field.
在不断扩展的数字领域中，像Booking和 TripAdvisor这样的消费平台上大量的用户生成内容，为旅行者和酒店经营者提供了丰富的信息来源。情感分析作为自然语言处理（NLP）的一项基础研究任务，被用于在这海量的文本评论中挖掘情感和观点。当需要处理客户评论时，会使用一种更具体的情感分析类型，即基于方面的情感分析（ABSA）。在ABSA中，我们旨在捕捉评论中各个方面的情感以及不同方面之间的复杂关系。本文提出了一种新颖的ABSA方法，引入了一种新颖的词义消歧（WSD）技术，并将其与Transformer架构的双向编码器表征（BERT）和图卷积网络（GCN）相结合。所提出的方法解决了单词的有趣歧义，并将评论数据表示为复杂的图结构，便于对不同方面之间的复杂关系进行建模。双向长短期记忆（BiLSTM）和GCN的结合被证明在捕捉各个方面之间的相互依赖关系方面是有效的，从而提供对客户情感的细致理解。实验是在RABSA数据集（一个经过增强且更丰富的酒店评论数据集合）上进行的，结果表明我们的方法优于先前的基线，展示了在ABSA中整合WSD的有效性。此外，消融研究证实了WSD模块对整体性能的重大贡献。而且，我们探索了不同的相似性度量，发现当使用WordNet在给定句子中识别单词的真实含义时，余弦相似性产生的结果最佳。我们工作的研究结果以及与我们工作相关的未来工作，引起了旅游和酒店业人士的极大兴趣。这项研究进一步推动了NLP技术在情感分析中的潜力概念。它强调，如果我们将NLP技术的潜力与最先进的机器学习框架相结合，就可以塑造这个领域的未来。
