Sentiment Analysis of Japanese Tourism Online Reviews

Yu, Chuanming; Zhu, Xingyu; Feng, Bolin; Cai, Lin; An, Lu

doi:10.2478/jdis-2019-0005

Abstract

Purpose

Online reviews on tourism attractions provide important references for potential tourists to choose tourism spots. The main goal of this study is conducting sentiment analysis to facilitate users comprehending the large scale of the reviews, based on the comments about Chinese attractions from Japanese tourism website 4Travel.

Design/methodology/approach

Different statistics- and rule-based methods are used to analyze the sentiment of the reviews. Three groups of novel statistics-based methods combining feature selection functions and the traditional term frequency-inverse document frequency (TF-IDF) method are proposed. We also make seven groups of different rules-based methods. The macro-average and micro-average values for the best classification results of the methods are calculated respectively and the performance of the methods are shown.

Findings

We compare the statistics-based and rule-based methods separately and compare the overall performance of the two method. According to the results, it is concluded that the combination of feature selection functions and weightings can strongly improve the overall performance. The emotional vocabulary in the field of tourism (EVT), kaomojis, negative and transitional words can notably improve the performance in all of three categories. The rule-based methods outperform the statistics-based ones with a narrow advantage.

Research limitation

Two limitations can be addressed: 1) the empirical studies to verify the validity of the proposed methods are only conducted on Japanese languages; and 2) the deep learning technology is not been incorporated in the methods.

Practical implications

The results help to elucidate the intrinsic characteristics of the Japanese language and the influence on sentiment analysis. These findings also provide practical usage guidelines within the field of sentiment analysis of Japanese online tourism reviews.

Originality/value

Our research is of practicability. Currently, there are no studies that focus on the sentiment analysis of Japanese reviews about Chinese attractions.

References

Abd-Elhamid, L., Elzanfaly, D., & Eldin, A.S. (2016). Feature-based sentiment analysis in online Arabic reviews. In Proceedings of 11^th International Conference on Computer Engineering & Systems (pp.260–265). IEEE. doi: 10.1109/ICCES.2016.7822011
Open DOI Search in Google Scholar Back to article
Akhtar, M. S, Gupta, D., & Ekbal, A. (2017). Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis. Knowledge-Based Systems, 125, 116–135. doi: 10.1016/j.knosys.2017.03.020
Open DOI Search in Google Scholar Back to article
Asghar, M.Z., Khan, A., Ahmad, S., Qasim, M., & Khan, I. A (2017). Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PloS One, 12(2), e0171649. doi: 10.1371/journal.pone.0171649
Open DOI Search in Google Scholar Back to article
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016) Enriching word vectors with subword information. arXiv:1607.04606v2.
Search in Google Scholar Back to article
Contratres, F.G., Alves-Souza, S.N., Filgueiras, L.V.L., & DeSouza, L.S. (2018). Sentiment analysis of social network data for cold-start relief in recommender systems. In Proceedings of World Conference on Information Systems and Technologies (pp.122–132). Springer, Cham. doi: 10.1007/978-3-319-77712-2_12
Open DOI Search in Google Scholar Back to article
Endo, D., Saito, M., & Yamamoto. (2006).The extraction of emotional representation by using dependency relation. In Proceedings of Natural Language Processing.
Search in Google Scholar Back to article
Fernández, A.M., Esuli, A., & Sebastiani, F. (2016). Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification. Journal of Artificial Intelligence Research, 55(1), 131–163. doi: 10.1613/jair.4762
Open DOI Search in Google Scholar Back to article
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., & Mikolov, T. (2018). Learning word vectors for 157 languages. In Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018).
Search in Google Scholar Back to article
Impana, P., & Kallimani, J.S. (2017). Cross-lingual sentiment analysis for Indian regional languages (pp.1–6). In Proceedings of International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques.
Search in Google Scholar Back to article
Ma, W., & Deng, Y. (2013). New feature weighting calculation method for short text. Journal of Computer Applications, 33(8), 2280–2292.
Search in Google Scholar Back to article
Manek, A.S., Shenoy, P.D., Mohan, M.C., & Venugopal, K.R. (2016). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 135–154. doi: 10.1007/s11280-015-0381-x
Open DOI Search in Google Scholar Back to article
Nakamura, A. (1979). Kanjo Hyogen Jiten Toukyouto Rokkou Press.
Search in Google Scholar Back to article
Omar, N., Albared, M., Al-Moslmi, T, &. Al-Shabi, A. (2014) A comparative study of feature selection and machine learning algorithms for Arabic sentiment classification. Information Retrieval Technology, 8870, 429–443. doi: 10.1007/978-3-319-12844-3_37
Open DOI Search in Google Scholar Back to article
Parlak, B., & Uysal, A.K. (2018). On Feature weighting and selection for medical document classification. Developments and Advances in Intelligent Systems and Applications (pp. 269–282). Springer, Cham.
Search in Google Scholar Back to article
Palakvangsa-Na-Ayudhya, S, Sriarunrungreung. V, Thongprasan, P., & Porcharoen, S. (2011) Nebular: A sentiment classification system for the tourism business. In Proceedings of 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp.293–298). IEEE. doi: 10.1109/JCSSE.2011.5930137
Open DOI Search in Google Scholar Back to article
Palaniappan, R., Sundaraj, K., & Sundaraj, S. (2014). A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signal. BMC Bioinformatics, 15(1), 223. doi: 10.1186/1471-2105-15-223
Open DOI Search in Google Scholar Back to article
Severyn, A., Moschitti, A., Uryupina, O., Plank, B., & Filippova, K. (2016). Multi-lingual opinion mining on YouTube. Information Processing and Management, 52(1), 46–60. doi: 10.1016/j.ipm.2015.03.002
Open DOI Search in Google Scholar Back to article
Sharma, A., & Dey, S. (2012). A comparative study of feature selection and machine learning techniques for sentiment analysis. In Proceedings of the 2012 ACM research in applied computation symposium (pp.1–7). ACM. doi: 10.1145/2401603.2401605
Open DOI Search in Google Scholar Back to article
Siddiqua, U.A., Ahsan, T., & Chy, A.N. (2017). Combining a rule-based classifier with weakly supervised learning for twitter sentiment analysis. In Proceedings of International Conference on Innovations in Science (pp.1–4), Engineering and Technology. doi: 10.1109/ICISET. 2016.7856499
Open DOI Search in Google Scholar Back to article
Song, W., Cai, Y., Wu, B., & Sun, T. (2012). A new active learning strategy in nearest neighbor classifier. In Proceedings of the International Conference on Machine Learning and Cybernetics (pp.729–734). Xi^’an, China. IEEE. doi: 10.1109/ICMLC.2012.6359015
Open DOI Search in Google Scholar Back to article
Soni A K. (2017). Multi-lingual sentiment analysis of Twitter data by using classification algorithms. In Proceedings of 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp.1–5). doi: 10.1109/ICECCT.2017.8117884
Open DOI Search in Google Scholar Back to article
Vulic, I., Smet, W.D., Tang, J., & Moens, MF. (2015). Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications. Information Processing & Management 51(1), 111–147. doi: 10.1016/j.ipm.2014.08.003
Open DOI Search in Google Scholar Back to article
Xia, R., Xu, F., Yu, J., Qi, Y. & Cambria, E (2016). Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis. Information Processing & Management, 52(1), 36–45. doi: 10.1016/j.ipm.2015.04.003
Open DOI Search in Google Scholar Back to article
Xiao, X., Lu, J., Yu, L., & Gong, H. (2015). Research on feature selection algorithm based on the lowest term frequency of CHI. Journal of Southwest University (Natural Science Edition), 37(6), 137–142.
Search in Google Scholar Back to article
Xu, F.Y., & Luo, Z.S. (2015). An improved approach to term weighting in automated text classification. Computer Engineering and Application, 4(1), 181–184.
Search in Google Scholar Back to article
Yang, W., Song, J.J., & Tang, J.Q. (2013). A study on the classification approach for Chinese MicroBlog subjective and objective sentences. Journal of Chongqing University of Technology (Natural Science), 27(1), 51–56.
Search in Google Scholar Back to article
Yang, Y.M., & Pedersen, J.O. (1997). A comparative study on feature selection in text categorization. In Proceedings of the 14^th International Conference on Machine Learning (pp. 412–420). Nashville, TN, USA.
Search in Google Scholar Back to article
Zhang, C.Z., & Zhou, Q.Q. (2018). Online investigation of users’ attitudes using automatic question answering. Online Information Review, 2018, 42(3), 419–435. doi: 10.1108/OIR-10-2016-0299
Open DOI Search in Google Scholar Back to article
Zhang, L. (2015) Aspect: eight summary of “Internet + tourism” industry trend in 2016. Retrieved from http://mi.chinabyte.com/299/13641299.html
Search in Google Scholar Back to article
Zhang, L., Jiang, L., Li, C., & Kong, G. (2016). Two feature weighting approaches for naive Bayes text classifiers. Knowledge-Based Systems, 100, 137–144. doi: 10.1016/j.knosys.2016.02.017
Open DOI Search in Google Scholar Back to article
Zheng, L., Wang, H., & Gao, S. (2015). Sentimental feature selection for sentiment analysis of Chinese online. International Journal of Machine Learning and Cybernetics, 9(1), 75–84.
Search in Google Scholar Back to article
Zhou, G.Y., Zhu Z.Y., He, T.T., & Hu, X.T. (2016). Cross-lingual sentiment classification with stacked auto-encoders. Knowledge and Information Systems, 47(1), 27–44. doi: 10.1007/s10115-015-0849-0
Open DOI Search in Google Scholar Back to article
Zin, H.M., Mustapha, N., Murad, M.A.A. & Sharef, N.M. (2018). Term weighting scheme effect in sentiment analysis of online movie reviews. Advanced Science Letters, 24(2), 933–937.
Search in Google Scholar Back to article