Suchergebnisse

Intro -- Acknowledgements -- Abstract -- Introductory Remarks -- Contents -- Abbreviations -- List of Figures -- List of Tables -- Part I State of the Field -- 1 Approaching Media Influence and its Mechanisms -- 1.1 Agenda Setting, Biases, and Tendentious and Fake News in the Media -- 1.2 The Influence of Media Reporting on Public Opinions -- 1.3 Media Influence on Consumers and Customers -- 1.4 Media Influence on Businesses -- 2 Connections and Relationships in Europe and Worldwide and their Impact -- 2.1 Social Relations Regarding the Movement of Goods, Services, and Capital and Potential Effects -- 2.2 Connections within the Movement of Labour and Potential Implications -- 2.3 Relationships Measured by Support of the EU by its Citizens -- 2.4 Media Influence on International Social Relationships -- 3 Approaching Tourism as a Driver for Research on International Relations -- 3.1 Current Tourism Demand Forecasting Techniques -- 3.2 Tourism Demand Forecasting in Online and Social Media -- 3.3 Machine Learning and Artificial Neural Networks in Tourism -- 4 Sentiment Analysis as a New Source of Information -- 4.1 Forecasting, Nowcasting, and Hidden Correlations Explored with Sentiment Analysis -- 4.2 Sentiment Analysis to Reveal and Explore Macroeconomic Phenomena -- 4.3 Sentiment Analysis in the Context of Business Administration -- 5 Technical Aspects of Sentiment Analysis of Online News Sources -- 5.1 Large-scale Retrieval of Big Data -- 5.2 Different Approaches of Computer-Based Sentiment Analysis -- 5.3 Insights into a Deeper Understanding of the Underlying Texts and Algorithms -- Part II Research on Online Media and the Impact on Consumers, Businesses and Society -- 6 Methodology and Research Design -- 6.1 Information Retrieval -- 6.2 Artificial Neural Network Sentiment Analysis -- 6.3 Sentiment Index Building.

This study examines the differences and similarities in the perceptions of popular tourist destinations between the English and German media in an empirical and quantitative way. The perception of a tourist destination country is measured by the sentiment in the media in the tourists' countries of origin—either the UK or Germany. The study measures such perceptions with the help of a Long Short Term Memory (LSTM) artificial neural network that analyzes the perceptions in publicly available news streams in the UK and Germany. The results show that the online media indeed generates different perceptions for some of the popular travel destinations. An equally interesting finding is that there is, in general, a broad consensus between the sentiments in the British and German media. Businesses in the tourism and hospitality sectors, news providers, political actors, and policymakers can use the methodology to analyze the source of this information gap and the impact on their operations and policies.