Abstract
In recent years, the increased use of digital tools within the Peruvian tourism industry has created a corresponding increase in revenues. However, both factors have caused increased competition in the sector that in turn puts pressure on small and medium enterprises’ (SME) revenues and profitability. This study aims to apply neural network based sentiment analysis on social networks to generate a new information search channel that provides a global understanding of user trends and preferences in the tourism sector. A working data-analysis framework will be developed and integrated with tools from the cloud to allow a visual assessment of high probability outcomes based on historical data, to help SMEs estimate the number of tourists arriving and places they want to visit, so that they can generate desirable travel packages in advance, reduce logistics costs, increase sales, and ultimately improve both quality and precision of customer service.
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References
Confederación Nacional de Instituciones Empresariales Privadas - CONFIEP (2019). Turismo en Perú (21 Noviembre del 2019). Recuperado de https://www.confiep.org.pe/noticias/economia/turismo-en-peru/
Zapata, G., Murga, J., Raymundo, C., Dominguez, F., Moguerza, J.M., Alvarez, J.M.: Business information architecture for successful project implementation based on sentiment analysis in the tourist sector. J. Intell. Inf. Syst. 53(3), 563–585 (2019). https://doi.org/10.1007/s10844-019-00564-x
Zabha, N., Ayop, Z., Anawar, S., Erman, H., Zainal, Z.: Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach. Int. J. Adv. Comput. Sci. Appl. (2019). https://doi.org/10.14569/IJACSA.2019.0100146
Kincl, T., Novák, M., Pribil, J.: Improving sentiment analysis performance on morphologically rich languages: language and domain independent approach. Comput. Speech Lang. 56, 36–51 (2019). https://doi.org/10.1016/j.csl.2019.01.001
Fernández-Gavilanes, M., Juncal-Martńnez, J., Méndez, S., Costa-Montenegro, E., Castaño, F.: Differentiating users by language and location estimation in sentiment analisys of informal text during major public events. Expert Syst. Appl. 117 (2018). https://doi.org/10.1016/j.eswa.2018.09.007
Zvarevashe, K., Olugbara, O.: A framework for sentiment analysis with opinion mining of hotel reviews, pp. 1–4 (2018). https://doi.org/10.1109/ICTAS.2018.8368746
Gunasekar, S., Sudhakar, S.: Does hotel attributes impact customer satisfaction: a sentiment analysis of online reviews. J. Glob. Scholars Mark. Sci. 29, 180–195 (2019). https://doi.org/10.1080/21639159.2019.1577155
Anitsal, M.M., Anitsal, I., Anitsal, S.: Is your business sustainable? A sentiment analysis of air passengers of top 10 US-based airlines. J. Glob. Scholars Mark. Sci. 29, 25–41 (2019). https://doi.org/10.1080/21639159.2018.1552532
Alaei, A., Becken, S., Stantic, B.: Sentiment analysis in tourism: capitalizing on big data. J. Travel Res. 58, 004728751774775 (2017). https://doi.org/10.1177/0047287517747753
Ducange, P., Fazzolari, M., Petrocchi, M., Vecchio, M.: An effective Decision Support System for social media listening based on cross-source sentiment analysis models. Eng. Appl. Artif. Intell. 78, 71–85 (2019). https://doi.org/10.1016/j.engappai.2018.10.014
Suresh, H., Gladston, S.: An innovative and efficient method for Twitter sentiment analysis. Int. J. Data Min. Model. Manage. 11, 1 (2019). https://doi.org/10.1504/IJDMMM.2019.096543
Vural, A., Cambazoglu, B., Karagoz, P.: Sentiment-focused web crawling. ACM Trans. Web 8, 2020–2024 (2012). https://doi.org/10.1145/2396761.2398564
Haruechaiyasak, C., Kongthon, A., Palingoon, P., Trakultaweekoon, K.: S-sense: a sentiment analysis framework for social media sensing. In: IJCNLP 2013 Workshop on Natural Language Processing for Social Media (SocialNLP), pp. 6–13 (2013)
Zafra, S.M., Martín-Valdivia, M., Martínez-Cámara, E., López, L.: Studying the scope of negation for spanish sentiment analysis on Twitter. IEEE Trans. Affect. Comput. PP, 1 (2017). https://doi.org/10.1109/TAFFC.2017.2693968
Cai, K., Spangler, W., Chen, Y., Li, Z.: Leveraging sentiment analysis for topic detection. Web Intell. Agent Syst. 8, 291–302 (2010). https://doi.org/10.3233/WIA-2010-0192
You, Q.: Sentiment and emotion analysis for social multimedia: methodologies and applications, pp. 1445–1449 (2016). https://doi.org/10.1145/2964284.2971475
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61 (2014). https://doi.org/10.1016/j.neunet.2014.09.003
Sze, V., Chen, Y.-H., Yang, T.-J., Joel, E.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105 (2017). https://doi.org/10.1109/JPROC.2017.2761740
Rani, S., Kumar, P.: deep learning based sentiment analysis using convolution neural network. Arab. J. Sci. Eng. 44(4), 3305–3314 (2018). https://doi.org/10.1007/s13369-018-3500-z
Alharbi, A.S.M., de Doncker, E.: Twitter sentiment analysis with a deep neural network: an enhanced approach using user behavioral information. Cogn. Syst. Res. (2018). https://doi.org/10.1016/j.cogsys.2018.10.001
Du, C., Lei, H.: Sentiment analysis method based on piecewise convolutional neural network and generative adversarial network. Int. J. Comput. Commun. Control 4, 7–20 (2019). https://doi.org/10.15837/ijccc.2019.1.3374
Ju, H., Yu, H.: Sentiment classification with convolutional neural network using multiple word representations 1–7 (2018). https://doi.org/10.1145/3164541.3164610
Du, T., Huang, Y., Wu, X., Chang, H.: Multi-attention network for sentiment analysis. In: NLPIR 2018: Proceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval, pp. 49–54 (2018). https://doi.org/10.1145/3278293.3278295
Zapata, G., Murga, J., Raymundo, C., Alvarez, J., Dominguez, F.: Predictive model based on sentiment analysis for Peruvian SMEs in the sustainable tourist sector. In: IC3K 2017 - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, vol. 3, pp. 232–240 (2017)
Kotzias, D., et al.: From group to individual labels using deep features. In: KDD (2015)
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Murga, J. et al. (2020). A Sentiment Analysis Software Framework for the Support of Business Information Architecture in the Tourist Sector. In: Hameurlain, A., et al. Transactions on Large-Scale Data- and Knowledge-Centered Systems XLV. Lecture Notes in Computer Science(), vol 12390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62308-4_8
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