Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews
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 f...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2635.pdf |
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| author | Asma Nadeem Malik Muhammad Saad Missen Mana Saleh Al Reshan Muhammad Ali Memon Yousef Asiri Muhammad Ali Nizamani Mohammad Alsulami Asadullah Shaikh |
| author_facet | Asma Nadeem Malik Muhammad Saad Missen Mana Saleh Al Reshan Muhammad Ali Memon Yousef Asiri Muhammad Ali Nizamani Mohammad Alsulami Asadullah Shaikh |
| author_sort | Asma Nadeem |
| collection | DOAJ |
| description | 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, i.e., 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. |
| format | Article |
| id | doaj-art-1cd031bc84604f06955cdd1f94c5633c |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-1cd031bc84604f06955cdd1f94c5633c2025-01-15T15:05:20ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e263510.7717/peerj-cs.2635Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviewsAsma Nadeem0Malik Muhammad Saad Missen1Mana Saleh Al Reshan2Muhammad Ali Memon3Yousef Asiri4Muhammad Ali Nizamani5Mohammad Alsulami6Asadullah Shaikh7Information Technology, Islamia University, Bahawalpur, Punjab, PakistanInformation Technology, Islamia University, Bahawalpur, Punjab, PakistanInformation System, Najran University, Najran Province, Saudi ArabiaInformation Technology, University of Sindh, Jamshoro, Sindh, PakistanComputer Science, Najran University, Najran Province, Saudi ArabiaInformation Technology, University of Sindh, Jamshoro, Sindh, PakistanEmerging Technologies Research Lab (ETRL), Najran University, Najran Province, Saudi ArabiaInformation System, Najran University, Najran Province, Saudi ArabiaIn 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, i.e., 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.https://peerj.com/articles/cs-2635.pdfNatural languageSentiment analysisConvolutional networksHotel reviewsBidirectional long short-term memory |
| spellingShingle | Asma Nadeem Malik Muhammad Saad Missen Mana Saleh Al Reshan Muhammad Ali Memon Yousef Asiri Muhammad Ali Nizamani Mohammad Alsulami Asadullah Shaikh Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews PeerJ Computer Science Natural language Sentiment analysis Convolutional networks Hotel reviews Bidirectional long short-term memory |
| title | Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews |
| title_full | Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews |
| title_fullStr | Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews |
| title_full_unstemmed | Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews |
| title_short | Resolving ambiguity in natural language for enhancement of aspect-based sentiment analysis of hotel reviews |
| title_sort | resolving ambiguity in natural language for enhancement of aspect based sentiment analysis of hotel reviews |
| topic | Natural language Sentiment analysis Convolutional networks Hotel reviews Bidirectional long short-term memory |
| url | https://peerj.com/articles/cs-2635.pdf |
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