Research on Sentiment Classification of Online Travel Review Text
Source abstract
Abstract
In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Web Data Mining and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China
School of Mathematics and Computer Science, Tongling College, Tongling 244000, China
Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu 241003, China
Funding
National Natural Science Foundation of China
References (74)
Show source-provided references
1. Forecasting tourism demand with composite search index10.1016/j.tourman.2016.07.005 ↗
2. Critical analysis of Big Data challenges and analytical methods10.1016/j.jbusres.2016.08.001 ↗
3. Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation10.1016/j.tourman.2016.09.009 ↗
4. Co-creating social media agility to build strong customer-firm relationships10.1016/j.indmarman.2019.06.012 ↗
5. Kauffmann, E., Peral, J., Gil, D., Ferrandez, A., Sellers, R., and Mora, H. (2019). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Ind. Mark. Manag., in press.10.1016/j.indmarman.2019.08.003 ↗
6. Big data in tourism research: A literature review10.1016/j.tourman.2018.03.009 ↗
7. Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics10.1016/j.tourman.2015.07.018 ↗
8. Sentiment Analysis in Tourism: Capitalizing on Big Data10.1177/0047287517747753 ↗
9. T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme10.1111/exsy.12233 ↗
10. Multiaspect-based opinion classification model for tourist reviews10.1111/exsy.12371 ↗
11. Debating big data: A literature review on realizing value from big data10.1016/j.jsis.2017.07.003 ↗
12. Business intelligence and big data in hospitality and tourism: A systematic literature review10.1108/IJCHM-07-2017-0461 ↗
13. Automated Sentiment Analysis in Tourism: Comparison of Approaches10.1177/0047287517729757 ↗
14. Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling10.1016/j.trc.2017.01.014 ↗
15. Understanding hidden dimensions in textual reviews on Airbnb: An application of modified latent aspect rating analysis (LARA)10.1016/j.ijhm.2019.02.008 ↗
16. Opinion Mining and Sentiment Analysis10.1561/1500000011 ↗
17. Neidhardt, J., Rummele, N., and Werthner, H. (2016). Can We Predict Your Sentiments by Listening to Your Peers. Information and Communication Technologies in Tourism 2016, Springer.10.1007/978-3-319-28231-2_43 ↗
18. Automatic analysis of textual hotel reviews10.1007/s40558-015-0047-7 ↗
19. An, H., and Moon, N. (2019). Design of recommendation system for tourist spot using sentiment analysis based on CNN-LSTM. J. Ambient Intell. Humaniz. Comput., 1–11.10.1007/s12652-019-01521-w ↗
20. Pan, D., Yuan, J., Li, L., and Sheng, D. (2019). Deep neural network-based classification model for Sentiment Analysis. arXiv.10.1109/BESC48373.2019.8963171 ↗
21. Handhika, T., Fahrurozi, A., Sari, I., Lestari, D.P., and Zen, R.I. (2019, January 10–11). Hybrid Method for Sentiment Analysis Using Homogeneous Ensemble Classifier. Proceedings of the 2019 2nd International Conference of Computer and Informatics Engineering (IC2IE), Banyuwangi, Indonesia.
22. What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management10.1016/j.techfore.2017.01.001 ↗
23. Hinterstoisser, S., Lepetit, V., Wohlhart, P., and Konolige, K. (2018, January 8–14). On Pre-Trained Image Features and Synthetic Images for Deep Learning. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-11009-3_42 ↗
24. A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach10.1016/j.eswa.2018.04.006 ↗
25. An improved semi-supervised dimensionality reduction using feature weighting: Application to sentiment analysis10.1016/j.eswa.2018.05.023 ↗
26. Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification10.1016/j.asoc.2016.06.003 ↗
27. QER: A new feature selection method for sentiment analysis10.1186/s13673-018-0135-8 ↗
28. Hybrid sentiment classification on twitter aspect-based sentiment analysis
29. Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter10.1007/s11042-019-7278-0 ↗
30. Exploring overall opinions for document level sentiment classification with structural SVM10.1007/s00530-017-0550-0 ↗
31. Pengaruh Semantic Expansion pada Naïve Bayes Classifier untuk Analisis Sentimen Tokoh Masyarakat10.29207/resti.v3i2.901 ↗
32. Knowledge-enhanced neural networks for sentiment analysis of Chinese reviews10.1016/j.neucom.2019.08.054 ↗
33. Semantic Sentiment Analysis of Arabic Texts
34. The Impact of Sentiment Features on the Sentiment Polarity Classification in Persian Reviews10.1007/s12559-017-9513-1 ↗
35. Agarwal, B., and Mittal, N. (2016). Sentiment Analysis Using ConceptNet Ontology and Context Information. Prominent Feature Extraction for Sentiment Analysis, Springer.10.1007/978-3-319-25343-5 ↗
36. Xiang, Z., and Fesenmaier, D.R. (2017). Sensing the Online Social Sphere Using a Sentiment Analytical Approach. Analytics in Smart Tourism Design: Concepts and Methods, Springer International Publishing.
37. Aspect based Sentiment Oriented Summarization of Hotel Reviews10.1016/j.procs.2017.09.115 ↗
38. Sentiment analysis – a review and agenda for future research in hospitality contexts10.1108/IJCHM-10-2017-0704 ↗
39. Exploring Big Data Applied in the Hotel Guest Experience
40. Stepchenkova, S., Kirilenko, A.P., and Li, X. (2018). Barriers and Sentiment of the American Tourists Toward Travel to China. Tourist Behavior, Springer.10.1007/978-3-319-78553-0_9 ↗
41. Hybrid attribute based sentiment classification of online reviews for consumer intelligence10.1007/s10489-018-1299-7 ↗
42. Reviews and Price on Online Platforms: Evidence from Sentiment analysis of Airbnb reviews in Boston10.1016/j.regsciurbeco.2018.11.003 ↗
43. Sentiment Analysis in TripAdvisor10.1109/MIS.2017.3121555 ↗
44. A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism10.1016/j.tourman.2016.10.001 ↗
45. Social media analytics in hospitality and tourism: A systematic literature review and future trends
46. Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach10.1007/s10586-017-1693-7 ↗
47. Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods10.1016/j.neucom.2018.09.096 ↗
48. Schmunk, S., Hopken, W., Fuchs, M., and Lexhagen, M. (2013). Sentiment Analysis: Extracting Decision-Relevant Knowledge from UGC. Information and Communication Technologies in Tourism 2014, Springer.10.1007/978-3-319-03973-2_19 ↗
49. (2020, June 17). Natural Language Toolkit. Available online: http://www.nltk.org/.
50. Mihalcea, R., and Tarau, P. (2004, January 25–26). TextRank: Bringing Order into Texts. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain.
51. Textual keyword extraction and summarization: State-of-the-art10.1016/j.ipm.2019.102088 ↗
52. YAKE! Keyword extraction from single documents using multiple local features10.1016/j.ins.2019.09.013 ↗
53. Mining microblog user interests based on TextRank with TF-IDF factor10.1016/S1005-8885(16)60056-0 ↗
54. Ngonga Ngomo, A.-C., and Křemen, P. Thesaurus-Based Method of Increasing Text-via-Keyphrase Graph Connectivity During Keyphrase Extraction for e-Tourism Applications. Proceedings of the Knowledge Engineering and Semantic Web.
55. Gagliardi, I., and Artese, M.T. (2020). Semantic Unsupervised Automatic Keyphrases Extraction by Integrating Word Embedding with Clustering Methods. Multimodal Technol. Interact., 4.10.3390/mti4020030 ↗
56. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion10.1016/j.inffus.2020.06.008 ↗
57. Cheng, J., Wang, Z., Wen, J., Yan, J., and Chen, Z. (2015, January 19–23). Contextual Text Understanding in Distributional Semantic Space. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, Australia.10.1145/2806416.2806517 ↗
58. Wang, J., Wang, Z., Zhang, D., and Yan, J. (2017, January 19–25). Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Australia.10.24963/ijcai.2017/406 ↗
59. Rosso, P., Yang, D., and Cudremauroux, P. (2019, January 9–12). Revisiting Text and Knowledge Graph Joint Embeddings: The Amount of Shared Information Matters!. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.10.1109/BigData47090.2019.9005462 ↗
60. Wu, W., Li, H., Wang, H., and Zhu, K.Q. (2012, January 20–24). Probase: A probabilistic taxonomy for text understanding. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, Scottsdale, AZ, USA.10.1145/2213836.2213891 ↗
61. (2020, June 16). Microsoft Concept Graph and Concept Tagging Release. Available online: https://concept.research.microsoft.com/Home/Introduction.
62. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5–10). Distributed Representations of Words and Phrases and their Compositionality. Proceedings of the Neural Information Processing Systems, Lake Tahoe, NV, USA.
63. Mikolov, T., Chen, K., Corrado, G.S., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv.
64. Development of content-based SMS classification application by using Word2Vec-based feature extraction10.1049/iet-sen.2018.5046 ↗
65. A Fusion Model-Based Label Embedding and Self-Interaction Attention for Text Classification10.1109/ACCESS.2019.2954985 ↗
66. Liu, B. (2007). Web Data Mining, Springer Berlin Heidelberg.
67. (2020, June 17). Microsoft Knowledge Graph. Available online: https://concept.research.microsoft.com/Home/API.
68. (2020, June 16). SentiStrength. Available online: http://sentistrength.wlv.ac.uk/.
69. Editorial: Special issue on learning from imbalanced data sets10.1145/1007730.1007733 ↗
70. Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization10.1109/TSMCB.2009.2024166 ↗
71. GRASS: A Gradient-Based Random Sampling Scheme for Milano Retinex10.1109/TIP.2017.2686652 ↗
72. Manning, C., and Schutze, H. (1999). Foundations of Statistical Natural Language Processing, MIT Press.
73. Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation10.1109/ACCESS.2019.2928872 ↗
74. Dong, L., Huang, S., Wei, F., Lapata, M., Zhou, M., and Xu, K. (2017, January 3–7). Learning to Generate Product Reviews from Attributes. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain.10.18653/v1/E17-1059 ↗