This page has only limited features, please log in for full access.
Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset.
Ainhoa Serna; Aitor Soroa; Rodrigo Agerri. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability 2021, 13, 2397 .
AMA StyleAinhoa Serna, Aitor Soroa, Rodrigo Agerri. Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability. 2021; 13 (4):2397.
Chicago/Turabian StyleAinhoa Serna; Aitor Soroa; Rodrigo Agerri. 2021. "Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport." Sustainability 13, no. 4: 2397.
In recent years, digital technology and research methods have developed natural language processing for better understanding consumers and what they share in social media. There are hardly any studies in transportation analysis with TripAdvisor, and moreover, there is not a complete analysis from the point of view of sentiment analysis. The aim of study is to investigate and discover the presence of sustainable transport modes underlying in non-categorized TripAdvisor texts, such as walking mobility in order to impact positively in public services and businesses. The methodology follows a quantitative and qualitative approach based on knowledge discovery techniques. Thus, data gathering, normalization, classification, polarity analysis, and labelling tasks have been carried out to obtain sentiment labelled training data set in the transport domain as a valuable contribution for predictive analytics. This research has allowed the authors to discover sustainable transport modes underlying the texts, focused on walking mobility but extensible to other means of transport and social media sources.
Ainhoa Serna; Jon Kepa Gerrikagoitia. Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis. Advances in Business Information Systems and Analytics 2021, 180 -199.
AMA StyleAinhoa Serna, Jon Kepa Gerrikagoitia. Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis. Advances in Business Information Systems and Analytics. 2021; ():180-199.
Chicago/Turabian StyleAinhoa Serna; Jon Kepa Gerrikagoitia. 2021. "Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis." Advances in Business Information Systems and Analytics , no. : 180-199.
Public bike share (PBS) systems are meant to be a sustainable urban mobility solution in areas where different travel options and the practice of active transport modes can diminish the need on the vehicle and decrease greenhouse gas emission. Although PBS systems have been included in transportation plans in the last decades experiencing an important development and growth, it is crucial to know the main enablers and barriers that PBS systems are facing to reach their goals. In this paper, first, sentiment analysis techniques are applied to user generated content (UGC) in social media comments (Facebook, Twitter and TripAdvisor) to identify these enablers and barriers. This analysis provides a set of explanatory variables that are combined with data from official statistics and the PBS observatory in Spain. As a result, a statistical model that assesses the connection between PBS use and certain characteristics of the PBS systems, utilizing sociodemographic, climate, and positive and negative opinion data extracted from social media is developed. The outcomes of the research work show that the identification of the main enablers and barriers of PBS systems can be effectively achieved following the research method and tools presented in the paper. The findings of the research can contribute to transportation planners to uncover the main factors related to the adoption and use of PBS systems, by taking advantage of publicly available data sources.
Ainhoa Serna; Tomas Ruiz; Jon Kepa Gerrikagoitia; Rosa Arroyo. Identification of Enablers and Barriers for Public Bike Share System Adoption using Social Media and Statistical Models. Sustainability 2019, 11, 6259 .
AMA StyleAinhoa Serna, Tomas Ruiz, Jon Kepa Gerrikagoitia, Rosa Arroyo. Identification of Enablers and Barriers for Public Bike Share System Adoption using Social Media and Statistical Models. Sustainability. 2019; 11 (22):6259.
Chicago/Turabian StyleAinhoa Serna; Tomas Ruiz; Jon Kepa Gerrikagoitia; Rosa Arroyo. 2019. "Identification of Enablers and Barriers for Public Bike Share System Adoption using Social Media and Statistical Models." Sustainability 11, no. 22: 6259.
The fourth industrial revolution is characterized by the introduction of the Internet of things (IoT) and Internet of Services (IoS) concepts into manufacturing, which enables smart factories with vertically and horizontally integrated production systems. The main driver is technology, as Industry 4.0 is a collective term for technologies and concepts of value chain organization. Digital manufacturing platforms play an increasing role in dealing with competitive pressures and incorporating new technologies, applications, and services. Motivated by the difficulties to understand and adopt Industry 4.0 and the momentum that the topic has currently, this paper reviews the concepts and approaches related to digital manufacturing platforms from different perspectives: IoT platforms, digital manufacturing platforms, digital platforms as ecosystems, digital platforms from research and development perspective, and digital platform from industrial equipment suppliers.
Jon Kepa Gerrikagoitia; Gorka Unamuno; Elena Urkia; Ainhoa Serna. Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives. Applied Sciences 2019, 9, 2934 .
AMA StyleJon Kepa Gerrikagoitia, Gorka Unamuno, Elena Urkia, Ainhoa Serna. Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives. Applied Sciences. 2019; 9 (14):2934.
Chicago/Turabian StyleJon Kepa Gerrikagoitia; Gorka Unamuno; Elena Urkia; Ainhoa Serna. 2019. "Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives." Applied Sciences 9, no. 14: 2934.
The goal of the study of the paper is to propose a dashboard with dynamic graphics using a qualitatively and quantitatively approach to investigate the tourists’ satisfaction according by transport mode used. The methodology implemented in the research includes data collection from TripAdvisor.com with geographic locations and their integration with statistical territorial data. Text mining techniques are applied in order to assess tourists’ perceptions on success factors, which may be used as planning support tools. The case study concerns Croatia country and shows the value and complementarity of Social Media-related data with official statistics for transport and tourism planning.
Ainhoa Serna; Slaven Gasparovic. TRANSPORT ANALYSIS APPROACH BASED ON BIG DATA AND TEXT MINING ANALYSIS FROM SOCIAL MEDIA. Transportation Research Procedia 2018, 33, 291 -298.
AMA StyleAinhoa Serna, Slaven Gasparovic. TRANSPORT ANALYSIS APPROACH BASED ON BIG DATA AND TEXT MINING ANALYSIS FROM SOCIAL MEDIA. Transportation Research Procedia. 2018; 33 ():291-298.
Chicago/Turabian StyleAinhoa Serna; Slaven Gasparovic. 2018. "TRANSPORT ANALYSIS APPROACH BASED ON BIG DATA AND TEXT MINING ANALYSIS FROM SOCIAL MEDIA." Transportation Research Procedia 33, no. : 291-298.
The user-generated content (UGC) and web 2.0 have allowed substantial changes in the dynamics of the travel and tourism sector. Tourism marketing is conditioned by the importance of the Internet as a channel for promotion and marketing. Thus, UGC and social networks become a valuable source to achieve a proper management of the cognitive image that a visitor can have on a destination. Despite the great importance of the destination image there is not a universally accepted and validated model. The presented research work aims to contribute in the knowledge and understanding of the cognitive destination image of the Basque Country through the UGC. The present work aims to validate an image model of reference by means of digital content. The final goal is to produce the method, which enables to relate the perceived image by visitors with the projected image by marketing strategies driven by the DMO.
Ainhoa Serna; Jon Kepa Gerrikagoitia; Aurkene Alzua. Towards a Better Understanding of the Cognitive Destination Image of Euskadi-Basque Country Based on the Analysis of UGC. Information and Communication Technologies in Tourism 2014 2013, 395 -407.
AMA StyleAinhoa Serna, Jon Kepa Gerrikagoitia, Aurkene Alzua. Towards a Better Understanding of the Cognitive Destination Image of Euskadi-Basque Country Based on the Analysis of UGC. Information and Communication Technologies in Tourism 2014. 2013; ():395-407.
Chicago/Turabian StyleAinhoa Serna; Jon Kepa Gerrikagoitia; Aurkene Alzua. 2013. "Towards a Better Understanding of the Cognitive Destination Image of Euskadi-Basque Country Based on the Analysis of UGC." Information and Communication Technologies in Tourism 2014 , no. : 395-407.