Web数据抓取的价值:TripAdvisor的一个应用

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Gianluca Barbera, Luiz Araujo, Silvia Fernandes
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引用次数: 1

摘要

社交媒体分析(SMA)在当今的市场动态中越来越重要。然而,明智地使用它是必要的,无论是在推广任何一种产品/品牌,还是与客户互动。这需要它的有效理解和监测。一种方法是通过web数据抓取(WDS)工具,它允许选择网站和平台来比较它们的性能。他们可以优化提取社交媒体上发布的大数据。由于目前的挑战,一个可以特别利用这一来源的部门是旅游业(及其相关部门)。今年旅游业有望在大流行之后复苏,其影响仍在影响若干活动。许多商人和企业家已经使用了这些多功能工具。然而,他们真的知道自己的潜力吗?目前的研究强调了使用WDS从TripAdvisor的社交页面收集数据。除了比较竞争对手的表现,公司还获得了未被注意到的偏好/习惯的新知识。这有助于为他们和他们的客户带来更多有趣的创新和结果。本文使用的方法基于一个智能旅游咨询项目,从识别我们地区的差距开始,帮助旅游组织增强其数字形象和商业模式。在这个庞大的非结构化数据源中,无需编程就可以非常快速、轻松地检测到许多东西。此外,探索代码,无论是改进web scraper还是将其与其他平台/应用程序连接起来,都可以成为未来研究的对象,以利用消费者行为预测进行更高级的交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Value of Web Data Scraping: An Application to TripAdvisor
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.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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