2019冠状病毒病大流行对体育、视频游戏和旅游业的影响:媒体、互联网文章和Twitter数据的情绪分析

Łukasz Bryl, Justyna Majewska, Szymon Truskolaski
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引用次数: 3

摘要

目的:本章通过分析与Covid-19对这些行业影响相关的文章的情感叙述,研究大流行对体育,视频游戏和旅游业影响的程度和水平,从而评估和预测大流行期间和随后几年的行业状况,同时也解释了对特定行业积极情绪的来源。设计/方法/方法:本研究使用三个独立的R软件包,对媒体、在线文章和社交媒体(Twitter)上全球Covid-19大流行的披露情况进行了情绪分析。最终样本包括142篇文章;最早的一篇发表于2020年1月23日,而最新的一篇发表于2020年10月14日。研究发现:情感分析显示,与旅游相比,视频游戏和足球的文章的情感色彩要积极得多。就电子游戏和足球而言,“信任”或“期待”等积极情绪比旅游中常见的“恐惧”和“悲伤”情绪更受欢迎。大流行对电子游戏和足球的影响类似,既有负面影响,也有积极影响。研究局限/影响:除社交媒体信息外,进一步的研究应利用其他资源,如大众媒体或其他数据来源,并包括根据反应和情绪随时间变化而分为大流行阶段的长期分析。此外,应在未来的研究中确定影响对不同经济部门情况感知的因素。实际意义:情感分析的使用表明,在获得任何硬数据(例如财务数据)之前,可以对新的社会现象进行这种量化。社会影响:获得了一个近似值,用于量化社会对受大流行病影响的特定部门的"普遍感觉"。独创性和价值:这一章比较了不同行业对疫情危机的反应,揭示了对特定行业的增长或困难做出贡献的情绪。情绪分析的运用,使我们能够在没有确凿和全面的数据之前,对疫情期间的行业形势进行评估和预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects of the Covid-19 pandemic on sport, video game, and tourism industry: Sentiment analysis of press, internet articles, and Twitter data
Purpose: The chapter examines the extent and level of the pandemic impact on sport, video game, and tourism industry by analyzing the emotional narration of articles related to Covid-19 effects on these industries so as to assess and predict the situation of industries during the pandemic and in the following years, but also to explain sources of positive sentiment for a given industry. Design/methodology/approach: The study provides a sentiment analysis of the global disclosure of the Covid-19 pandemic in the press, online articles, and social media (Twitter) with the use of three independent R packages. The final sample consisted of 142 articles; the oldest was published on January 23, 2020, whereas the newest one on October 14, 2020. Findings: Sentiment analysis revealed that the emotional tinge of the articles is much more positive for video games and soccer than in the case of tourism. In the case of video games and soccer, positive emotions such as “trust” or “anticipation” prevailed over much more common emotions of “fear” and “sadness” used about tourism. The impact of the pandemic was similar for video games and soccer, which was a mixture of negative and positive events. Research limitations/implications: Further research should use other resources such as the mass media or other data sources in addition to social media information and include a long-term analysis divided into stages of the pandemic as reactions and moods have been changing over time. Moreover, the factors influencing the perception of situations in different sectors of the economy should be identified in future research. Practical implications: The use of sentiment analysis shows that such quantification may be performed for new social phenomena before any hard (e.g., financial) data are available. Social implications: An approximation was obtained for quantifying the societal “general feeling” with regards to specific sectors affected by the pandemic. Originality and value: The chapter compares the response to the pandemic crisis of different sectors that reveal the sentiment contributing to the growth or difficulties of a given industry. The use of sentiment analysis enabled us to assess and predict the situation of industries during the pandemic before the hard and comprehensive data will occur.
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