The value of Web Data Scraping: An application to TripAdvisor

• Autor(a) principal: Barbera, Gianluca
• Data de Publicação: 2023
• Outros Autores: Araujo, Luiz, Fernandes, Silvia
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/20019
• topic: Social media; Data scraping; Tourism; Smart consultancy; Cognitive system

Social Media Analytics (SMA) is more and more relevant in today’s market dynamics. However, it is necessary to use it wisely, either in promoting any kind of product/brand, or interacting with customers. This requires its effective understanding and monitoring. One way is through web data scraping (WDS) tools that allow to select sites and platforms to compare them in their performances. They can optimize extraction of big data published on social media. Due to current challenges, a sector that can particularly take advantage of this source is tourism (and its related sectors). This year has the hope of tourism’s revival after a pandemic whose impacts are still affecting several activities. Many traders and entrepreneurs have already used these versatile tools. However, do they really know their potential? The present study highlights the use of WDS to collect data from TripAdvisor’s social pages. Besides comparing competitors’ performance, companies also gain new knowledge of unnoticed preferences/habits. This contributes to more interesting innovations and results for them and for their customers. The approach used here is based on a project for smart tourism consultancy, from the identification of a gap in our region, to aid tourism organizations to enhance their digital presence and business model. Many things can be detected in this big source of unstructured data very quickly and easily without programming. Moreover, exploring code, either to refine the web scraper or connect it with other platforms/apps, can be an object of future research to leverage consumer behavior prediction for more advanced interactions.