33,215 research outputs found
From Blockbuster to Neighbourhood Buster: The Effect of Films on Barcelona
In recent years, cities such as Venice, Dubrovnik, Paris and Barcelona have experienced
an exponential increase in visitor numbers leading to episodes of tourismphobia by anti-tourism
movements, or even the decline of the destination. Among other solutions, some destinations see
film-induced tourism as a possible way of diversifying tourism supply and demand. Through the
analysis of the locations of six thematic film routes in Barcelona compared to the same locations on
the largest online travel review platform, TripAdvisor, it is concluded that, far from spreading out
tourist flows, fiction-induced tourism in Barcelona has concentrated tourism at the main attractions
of the city. Only a few exceptions of films with minor audiences lead tourists off the beaten track.
Overall, this paper provides a set of recommendations, strategies and challenges for destination
managers to help alleviate overtourism and to offer more sustainable tourism away from spots that
attract mass tourism.This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness (grants ID ECO2017-88984-R, TIN2015-71799-C2-2-P, and HAR2016-77734-P), and the support of the Institute of Social Development and Territory INDEST of University of Lleida (call 2018CRINDESTABC). First author also acknowledges the support of the Spanish Education Ministry for the abroad mobility stay âJosĂ© Castillejoâ (Ref. Number CAS19/00362)
Co-evolution of Content Popularity and Delivery in Mobile P2P Networks
Mobile P2P technology provides a scalable approach to content delivery to a
large number of users on their mobile devices. In this work, we study the
dissemination of a \emph{single} content (e.g., an item of news, a song or a
video clip) among a population of mobile nodes. Each node in the population is
either a \emph{destination} (interested in the content) or a potential
\emph{relay} (not yet interested in the content). There is an interest
evolution process by which nodes not yet interested in the content (i.e.,
relays) can become interested (i.e., become destinations) on learning about the
popularity of the content (i.e., the number of already interested nodes). In
our work, the interest in the content evolves under the \emph{linear threshold
model}. The content is copied between nodes when they make random contact. For
this we employ a controlled epidemic spread model. We model the joint evolution
of the copying process and the interest evolution process, and derive the joint
fluid limit ordinary differential equations. We then study the selection of the
parameters under the content provider's control, for the optimization of
various objective functions that aim at maximizing content popularity and
efficient content delivery.Comment: 21 pages, 16 figure
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
Destination image analytics through traveller-generated content
The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users' opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability
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
City image, city brand personality and Generation Z residentsâ life satisfaction under economic crisis: Predictors of city-related social media engagement
The originality of the present study lies in that it examines generation Z residentsâ engagement with the cityâs social media during economic crisis in relation to city image, city brand personality and residentsâ overall satisfaction. In order to test our hypotheses, 947 usable questionnaires were collected in Thessaloniki, Greece via the mall intercept technique. The findings reveal the significant impact city image and city brand personality have on generation Z residentsâ engagement with cityâs social media. The results also demonstrate a negative linkage between residentsâ overall satisfaction and their engagement with the cityâs social media. Lastly, the results support that the relationship between residents' overall satisfaction and their engagement with city's social media accounts is moderated by the effect of economic crisis on residents' personal daily routine. Implications for theory and practice are also discussed
Organic Exporter Guide
This guide was developed for the programme 'Export Promotion of Organic Products from Africa' (EPOPA), implemented by Agro Eco and Grolink. The focus of this guide is on export marketing of organic agricultural products. Finished consumer products and other concepts of certification such as Fair Trade and EurepGAP are briefly discussed as well. The guide is written for African exporters starting with organic exports. It may also be useful for business supporters involved in export marketing
Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models
Aquesta tesi proposa diversos mĂštodes avançats per analitzar automĂ ticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells dâanĂ lisi i en diferents idiomes.
Comencem proposant un sistema dâanĂ lisi de sentiments, anomenat SentiRich, basat en un conjunt ric dâatributs, inclosa la informaciĂł extreta de lĂšxics de sentiments i models de word embedding pre-entrenats. A continuaciĂł, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sĂšrie de tasques dâanĂ lisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques tĂpiques dâanĂ lisi de sentiments fins a determinar automĂ ticament la intensitat dâuna emociĂł (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. TambĂ© proposem un nou sistema basat en Deep Learning per solucionar el problema de classificaciĂł de les emocions mĂșltiples a Twitter. A mĂ©s, es va considerar el problema de lâanĂ lisi del sentiment depenent de lâobjectiu. Per a aquest propĂČsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com lâanglĂšs, disposen dâuna Ă mplia gamma de recursos per permetre lâanĂ lisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tĂšcnica d'anĂ lisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingĂŒe i basat en Deep Learning per a llenguatges amb pocs recursos lingĂŒĂstics. Proposem combinar lâajuda a la presa de decisions multi-criteri i anĂ lisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat dâexplotar tant les opinions com les seves preferĂšncies en el procĂ©s de classificaciĂł dâalternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicaciĂł de les marques de destinaciĂł a travĂ©s de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turĂstiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mĂštodes proposats en aquesta tesi milloren el rendiment dels enfocaments dâĂșltima generaciĂł i mostren troballes apassionants.Esta tesis propone varios mĂ©todos avanzados para analizar automĂĄticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de anĂĄlisis y en diferentes idiomas. Comenzamos proponiendo un sistema de anĂĄlisis de sentimientos, llamado SentiRich, que estĂĄ basado en un conjunto rico de caracterĂsticas, que incluyen la informaciĂłn extraĂda de lĂ©xicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de anĂĄlisis de sentimientos y emociones en Twitter. Estas tareas van desde las tĂpicas tareas de anĂĄlisis de sentimientos hasta la determinaciĂłn automĂĄtica de la intensidad de una emociĂłn (como alegrĂa, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. TambiĂ©n proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificaciĂłn de emociones mĂșltiples en Twitter. AdemĂĄs, consideramos el problema del anĂĄlisis de sentimientos dependiente del objetivo. Para este propĂłsito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets.
Si bien algunos idiomas, como el inglĂ©s, tienen una amplia gama de recursos para permitir el anĂĄlisis de sentimientos, la mayorĂa de los idiomas carecen de ellos. Por lo tanto, utilizamos la tĂ©cnica de AnĂĄlisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingĂŒe y basado en Deep Learning para los lenguajes con pocos recursos lingĂŒĂsticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el anĂĄlisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificaciĂłn de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicaciĂłn de las marcas de destino a travĂ©s de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turĂsticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los mĂ©todos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify peopleâ opinions, emotions and feelings at a different level of analysis and in different languages.
We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets.
While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets
Analysing the Differential Performances of Indian States in the Tourism Sector : (1947-early 2020)
Tourism is an ever evolving and a dynamic industry which can play a crucial role in increasing income and in providing employment opportunities in an economy. India especially with its rich heritage, culture and geographical landscapes has always had immense potential to become a leading tourist destination. Presently the major types of tourism prevalent in India are Medical Tourism, Rural/ Natural Tourism, Religious Tourism and Historical& Educational Tourism. In 2018-19, the tourism sector contributed around 5% to Indiaâs GDP. However with the health shock of Covid-19, the tourism sector has taken a major hit since early 2020, with several people losing their jobs in the tourism and hospitality sector when different states imposed lockdowns and took various measures to curb the pandemic. As restrictions in each state eased during the first wave of the pandemic, different states in India adopted various policies to revive the tourism industry. But to understand the effectiveness of these policies in each state/ UT, one needs to investigate the baseline at which the Tourism industry was before the pandemic hit the country. This paper attempts to look at the differential performances of states and UTs of India in tourism by categorizing them into various types of tourism between 1947 until March 2020.This paper aims to act as a base for further analysing the impact of this pandemic on Tourism across states in India
- âŠ