YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018

Attitudes expressed in online comments about environmental factors in the tourism sector: an exploratory study

The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified

Information Technology Applications in Hospitality and Tourism: A Review of Publications from 2005 to 2007

The tourism and hospitality industries have widely adopted information technology (IT) to reduce costs, enhance operational efficiency, and most importantly to improve service quality and customer experience. This article offers a comprehensive review of articles that were published in 57 tourism and hospitality research journals from 2005 to 2007. Grouping the findings into the categories of consumers, technologies, and suppliers, the article sheds light on the evolution of IT applications in the tourism and hospitality industries. The article demonstrates that IT is increasingly becoming critical for the competitive operations of the tourism and hospitality organizations as well as for managing the distribution and marketing of organizations on a global scale

Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020