Abstract
Social media is one of the data sources that could provide more information or potential knowledge in almost any field of application. One of the main challenges of machine learning and big data is to solve the difficulty involved in the identification, classification, and, in general, the processing of all this data to extract useful information for a specific field. In this work, we propose a methodology for the detection of tourist places of interest through the combined use of images and text from social networks. For that purpose, we will be assisted by pre-trained neural networks for image classification and sentiment analysis. The result is frequency information of types of places according to a tourism-specific taxonomy combined with user sentiment indicators, which is potentially relevant information for tourism analysts.
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Acknowledgement
This work was funded by the University of Alicante UAPOSTCOVID19-10 grant for “Collecting and publishing open data for the revival of the tourism sector post-COVID-19” project. We would like to thank Nvidia for their generous hardware donations that made these experiments possible.
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Lucas, L., Tomás, D., Garcia-Rodriguez, J. (2022). Sentiment Analysis and Image Classification in Social Networks with Zero-Shot Deep Learning: Applications in Tourism. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_40
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DOI: https://doi.org/10.1007/978-3-030-87869-6_40
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