Predictive model based on sentiment analysis for peruvian smes in the sustainable tourist sector

In the sustainable tourist sector today, there is a wide margin of loss in small and medium-sized enterprise (SMEs) because of a poor control in logistical expenses. In other words, acquired goods are note being sold, a scenario which is very common in tourism SMEs. These SMEs buy a number of travel packages to big companies and because of the lack of demand of said packages, they expire and they become an expense, not the investment it was meant to be. To solve this problem, we propose a Predictive model based on sentiment analysis of a social networks that will help the sales decision making. Once the data of the social network is analyzed, we also propose a prediction model of tourist destinations, using this information as data source it will be able to predict the tourist interest. In addition, a case study was applied to a real Peruvian tourist enterprise showing their data before and after using the proposed model in order to validate the feasibility of proposed model

Business information architecture for successful project implementation based on sentiment analysis in the tourist sector

In the today's market, there is a wide range of failed IT projects in specialized small and medium-sized companies because of poor control in the gap between the business and its vision. In other words, acquired goods are not being sold, a scenario which is very common in tourism retail companies. These companies buy a number of travel packages from big companies and due to lack of demand for these packages, they expire, becoming an expense, rather than an investment. To solve this problem, we propose to detect the problems that limit a company by re-engineering the processes, enabling the implementation of a business architecture based on sentimental analysis, allowing small and medium-sized tourism enterprises (SMEs) to make better decisions and analyze the information that most possess, without knowing how to exploit it. In addition, a case study was applied using a real company, comparing data before and after using the proposed model in order to validate feasibility of the applied model.This work has been partially funded by the following projects of the Spanish Ministry of Science, Innovation and Universities GROMA (MTM2015-63710-P), MODAS-IN (reference: RTI2018-094269-B-I00), PPI (RTC-2015-3580-7) and UNIKO (RTC-2015-3521-7), and the “methaodos.org” research group at URJC

Twitter usage in Tourism: Literature Review

Background: Twitter is the most popular microblog platform. Individuals, companies, organizations, and even governments use Twitter on a daily bases and get vast benefits from it. Twitter also has been valuable for the tourism sector, especially in developing business strategies, planning and studying tourist decision-making processes. Objectives: Goal of the paper is to identify the trends, patterns and the research gaps of the research focusing on the Twitter usage in tourism. Methods/Approach: A bibliometric analysis was conducted in order to identify significant authors, journals, and institutions who engaged in the research-oriented towards Twitter utilization in tourism. In addition, text-mining analysis has been conducted in order to extract and identify the topics of the papers investigating the utilization of Twitter for tourism research. Results: Research of Twitter utilization in tourism has increased substantially in the last decade, with most of the research conducted in the United States and Japan. Extracted topics are focused on distinctive themes, such as network analysis, word of mouth, and destination management. Conclusions: New topics have emerged, such as the utilization of Twitter in crisis communication and terrorist attacks, as well as the integration of Twitter and other social media such as Flickr

Cartoons as interdiscourse : a quali-quantitative analysis of social representations based on collective imagination in cartoons produced after the Charlie Hebdo attack

The attacks against Charlie Hebdo in Paris at the beginning of the year 2015 urged many cartoonists – most professionals but some laymen as well – to create cartoons as a reaction to this tragedy. The main goal of this article is to show how traumatic events like this one can converge in a rather limited set of metaphors, ranging from easily recognizable topoi to rather vague interdiscourses that circulate in contemporary societies. To do so, we analyzed 450 cartoons that were produced as a reaction to the Charlie Hebdo attacks, and took a quali-quantitative approach that draws both on discourse analysis and semiotics. In this paper, we identified eight main themes and we analyzed the five ones which are anchored in collective imagination (the pen against the sword, the journalist as a modern hero, etc.). Then, we studied the cartoons at figurative, narrative and thematic levels thanks to Greimas’ model of the semiotic square. This paper shows the ways in which these cartoons build upon a memory-based network of events from the recent past (particularly 9/11), and more generally on a collective imagination which can be linked to Western values.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Assessing the social impacts of extreme weather events using social media

The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Marketing for Sustainable Tourism

The aim of the Special Issue is to discuss the main current topics concerning marketing for sustainable tourism with reference to territories (i.e., tourism destinations, protected areas, parks and/or natural sites, UNESCO World Heritage Sites, rural regions/areas, etc.) and tourism enterprises and/or organisations (i.e., destination management organisations, hospitality enterprises, restaurant enterprises, cableway companies, travel agencies, etc.). In destinations where natural resources are pull factors for tourism development, the relationships among local actors (public, private, and local community), as well as marketing choices, are essential to develop sustainable tourism products. To this end, the Special Issue encourages papers that analyse marketing strategies adopted by tourism destinations and/or tourism enterprises to avoid overtourism, to manage mass sustainable tourism (as defined by Weaver, 2000), and to encourage and promote sustainable tourism in marginal areas or in territories suffering lack of integration in the tourism offer. Special attention will be given to contributions on the best practices to manage territories and/or enterprises adopting sustainable marketing strategies

Information and Communication Technologies in Tourism 2022

This open access book presents the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 29th Annual International eTourism Conference, which assembles the latest research presented at the ENTER2022 conference, which will be held on January 11–14, 2022. The book provides an extensive overview of how information and communication technologies can be used to develop tourism and hospitality. It covers the latest research on various topics within the field, including augmented and virtual reality, website development, social media use, e-learning, big data, analytics, and recommendation systems. The readers will gain insights and ideas on how information and communication technologies can be used in tourism and hospitality. Academics working in the eTourism field, as well as students and practitioners, will find up-to-date information on the status of research

Quantifying human behaviour with online images

From online searches to social media posts, our everyday interactions with the Internet are creating vast amounts of data. Large volumes of this data can be accessed rapidly at low cost, opening up unprecedented possibilities to monitor and analyse social processes and measure human behaviour. As Internet connectivity has continued to improve, photo-sharing platforms such as Instagram and Flickr have gained widespread popularity. At the same time, considerable advances have been achieved in the power of computers to analyse the contents of images. In particular, deep learning based methods such as convolutional neural networks have radically transformed the performance of systems seeking to identify objects in images, or classify the contents of a scene. Here, we showcase a series of studies in which we seek to quantify various aspects of human behaviour by exploiting both the large quantities of photographic data shared online and recent developments in computer vision. Specifically, we investigate whether data extracted from photographs shared on Flickr and Instagram can help us track global protest outbreaks; estimate the income of inhabitants living in different areas of London and New York; and predict the occurrence of noise complaints in New York City. Our findings are in line with the striking hypothesis that information extracted through automatic analysis of photographs shared online may help us measure human behaviour, whether in individual cities or across the glob