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Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2022)

Abstract

Currently, social networks, where people can express their opinion through content and comments, are fast developing and affect various areas of daily life; Particularly, some research on YouTube travel channels found that almost tourists and audiences leave comments about their attitudes to that place. Thus, mining the emotional recognition of comments through artificial intelligence can bring knowledge about the tourists’ general view. This article analyzes the relationship(s) between social media use and its effect on community-based tourism in Thailand using the Social Media Sensing framework (S-Sense) as sentiment analysis and the Bidirectional Long Short-Term Memory (BiLSTM) methods to analyze the text comments. This research collected 51,280 comments on 114 Youtube Videos, which are tourist attractions in various provinces in Thailand. The approach categorizes attractions based on sentiment analysis of 60% or more, including restaurants, markets, historical sites, temples, or natural attractions. The results show that 67.51% of the 19,391 clean-processed comments were satisfied with those attraction places. Therefore S-Sense and BiLSTM models can be sufficient to analyze the attitude of comments about attraction places with from 43 to remain 33 keywords of 1,603 comments. Furthermore, the offered sentiment analysis method has higher precision, recall, and F1 scores.

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References

  1. Lee, T.H., Jan, F.-H.: Can community-based tourism contribute to sustainable development? Evidence from residents perceptions of the sustainability. Tourism Manag. 70, 368–380 (2019)

    Article  Google Scholar 

  2. Melphon Mayaka, W., Croy, G., Cox, J.: A dimensional approach to community-based tourism: Recognising and differentiating form and context. Ann. Tourism Res. 74, 177–190 (2019). https://doi.org/10.1016/j.annals.2018.12.002

    Article  Google Scholar 

  3. Gu, X., Wu, J., Guo, H., Li, G.: Local tourism cycle and external business cycle. Ann. Tourism Res. 73, 159–170 (2018)

    Article  Google Scholar 

  4. Laparojkit, S., Suttipun, M.: The influence of customer trust and loyalty on repurchase intention of domestic tourism: a case study in Thailand during COVID-19 crisis. The J. Asian Finance, Econ. Bus. 8(5), 961–969 (2021)

    Google Scholar 

  5. de Vries, D.A., Peter, J., de Graaf, H., Nikken, P.: Adolescents’ social network site use, peer appearance-related feedback, and body dissatisfaction: testing a mediation model. J. Youth Adolesc. 45, 211–224 (2016)

    Article  Google Scholar 

  6. Saiphoo, A.N., Halevi, L.D., Vahedi, Z.: Social networking site use and self-esteem: a meta-analytic review. Pers. Individ. Differ. 153, 109639 (2020)

    Article  Google Scholar 

  7. Kolokytha, E., Loutrouki, S., Valsamidis, S., Florou, G.: Social media networks as a learning tool. Procedia Econ. Financ. 19, 287–295 (2015)

    Article  Google Scholar 

  8. Havakhor, T., Soror, A.A., Sabherwal, R.: Diffusion of knowledge in social media networks: effects of reputation mechanisms and distribution of knowledge roles. Inform. Syst. J. 28(1), 104–141 (2018)

    Article  Google Scholar 

  9. Lee, I.: Social media analytics for enterprises: Typology, methods, and processes. Bus. Horiz. 61(2), 199–210 (2018)

    Article  Google Scholar 

  10. Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)

    Article  Google Scholar 

  11. Bengio, Y., LeCun, Y., Hinton, G.: Deep learning for AI. Commun. ACM 64(7), 58–65 (2021)

    Article  Google Scholar 

  12. Haruechaiyasak, C., Kongthon, A., Palingoon, P., Trakultaweekoon, K.: S-sense: a sentiment analysis framework for social media Monitoring Applications. Inform. Technol. J. 14(1), 11–22 (2018)

    Google Scholar 

  13. Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X.: Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7, 51522–51532 (2019)

    Article  Google Scholar 

  14. Saggi, M.K., Jain, S.: A survey towards an integration of big data analytics to big insights for value-creation. Inform. Process. Manage. 54(5), 758–790 (2018)

    Article  Google Scholar 

  15. Olivera, P., Danese, S., Jay, N., Natoli, G., Peyrin-Biroulet, L.: Big data in IBD: a look into the future. Nat. Rev. Gastroenterol. Hepatol. 16, 312–321 (2019)

    Article  Google Scholar 

  16. Zakir, J., Seymour, T., Berg, K.: Big data analytics. Issues Inform. Syst. 16(2), 81–90 (2015)

    Google Scholar 

  17. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  18. Phiphitphatphaisit, S., Surinta, O.: Deep feature extraction technique based on Conv1D and LSTM network for food image recognition. Eng. Appl. Sci. Res. 48(5), 581–592 (2021)

    Google Scholar 

  19. Stieglitz, S., Mirbabaie, M., Ross, B., Neuberger, C.: Social media analytics – Challenges in topic discovery, data collection, and data preparation. Int. J. Inform. Manag. 39, 156–168 (2018)

    Article  Google Scholar 

  20. Holsapple, C., Hsiao, S.-H., Pakath, R.: Business social media analytics: Definition, benefits, and challenges. In: Twentieth Americas Conference on Information Systems (AMCIS), pp. 1–12. Savannah (2014).

    Google Scholar 

  21. Khruahong, S., Asawasakulson, A., Krom, W.N.: Social media analytics in comments of multiple vehicle brands on social networking sites in Thailand. In: Luo, Y. (ed.) Cooperative Design, Visualization, and Engineering (CDVE). Lecture Notes in Computer Science, vol. 12341, pp. 357–367. Springer, Cham (2020)

    Chapter  Google Scholar 

  22. Andryani, R., Negara, E.S., Triadi, D.: Social media analytics: data utilization of social media for research. J. Inform. Syst. Informatics 1(2), 193–205 (2019)

    Article  Google Scholar 

  23. Brooker, P., Barnett, J., Cribbin, T.: Doing social media analytics. Big Data Soc. 3, 205395171665806 (2016)

    Article  Google Scholar 

  24. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)

    Article  Google Scholar 

  25. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  26. Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press (2020). https://doi.org/10.1017/9781108639286

    Book  Google Scholar 

  27. Darwich, M., Noah, S.A.M., Omar, N.: Minimally-supervised sentiment lexicon induction model: a case study of malay sentiment analysis. In: Phon-Amnuaisuk, S., Ang, SP., Lee, Sy. (eds.) Multi-disciplinary Trends in Artificial Intelligence (MIWAI). Lecture Notes in Computer Science(), vol. 10607, pp. 225–237. Springer, Cham (2017).

    Google Scholar 

  28. Siami-Namini, S., Tavakoli, N., Namin, A.S.: The performance of LSTM and BiLSTM in forecasting time series. In: IEEE International Conference on Big Data (Big Data), pp. 3285–3292. IEEE, CA, USA (2019)

    Google Scholar 

  29. Maghfour, M., Elouardighi, A.: Standard and dialectal Arabic text classification for sentiment analysis. In: Abdelwahed, E., Bellatreche, L., Golfarelli, M., Méry, D., Ordonez, C. (eds.) Model and Data Engineering (MEDI). Lecture notes in Computer Science(), vol. 11163, pp. 282–291. Springer, Cham (2018)

    Chapter  Google Scholar 

  30. Tapsai, C., Meesad, P., Unger, H.: An overview on the development of Thai natural language processing. Inform. Technol. J. 15(2), 45–52 (2019)

    Google Scholar 

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Correspondence to Sanya Khruahong .

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Khruahong, S., Surinta, O., Lam, S.C. (2022). Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM. In: Surinta, O., Kam Fung Yuen, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2022. Lecture Notes in Computer Science(), vol 13651. Springer, Cham. https://doi.org/10.1007/978-3-031-20992-5_15

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  • DOI: https://doi.org/10.1007/978-3-031-20992-5_15

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