An improved model for sentiment analysis on luxury hotel review

This article proposes a heuristic model for sentiment analysis on luxury hotel reviews to analyse and explore marketing insights from attitudes and emotions expressed in reviews. We make several significant contributions to visual and multimedia analytics. This research will develop the practical application of visual and multimedia analytics as the research foundation is based on information analytics, geospatial analytics, statistical analytics and data management. Large amounts of data are generated by hotel customers on the Internet, which provides a good opportunity for managers and analysts to explore the hidden information. The analysis of luxury hotels involves different types of data, including real-world scale data, high-dimensional data and geospatial data. The diversity of data increases the difficulty of processing computational visual analytics. It leads to that some classical classification methods, which cost too much time and have high requirements for hardware, are excluded. The goal is to achieve a compromise between performance and cost. An experiment of this model is operated using data extracted from Booking.com. The entire framework of this experiment includes data collection, data preprocessing, feature engineering consisting of term frequency-inverse document frequency and Doc2Vec based feature generation and feature selection, Random Forest classification, data analysis and data visualization. The whole process combines statistical analysis, review sentiment analysis and visual analysis to make full use of this dataset and gain more decision-making information to improve luxury hotels' service quality. Compared with simple sentiment analysis, this integrated analytics in social media is expected to be used in practice to gain more insights. The result shows that luxury hotels should focus on staff training, cleanness of rooms and location choice to improve customer satisfaction. The sentiment distribution shows that scores are consistent with the emotion they show in reviews. Hotels in Spain have a much better average score than hotels in the other five countries. In the experiment, the sentiment analysis model is evaluated by receiver operating characteristic and precision-recall curve. It is proved that this model performs well. Twenty most essential features have been listed for future adjustments to the model

Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future

FORGE: An eLearning Framework for Remote Laboratory Experimentation on FIRE Testbed Infrastructure

The Forging Online Education through FIRE (FORGE) initiative provides educators and learners in higher education with access to world-class FIRE testbed infrastructure. FORGE supports experimentally driven research in an eLearning environment by complementing traditional classroom and online courses with interactive remote laboratory experiments. The project has achieved its objectives by defining and implementing a framework called FORGEBox. This framework offers the methodology, environment, tools and resources to support the creation of HTML-based online educational material capable accessing virtualized and physical FIRE testbed infrastruc- ture easily. FORGEBox also captures valuable quantitative and qualitative learning analytic information using questionnaires and Learning Analytics that can help optimise and support student learning. To date, FORGE has produced courses covering a wide range of networking and communication domains. These are freely available from FORGEBox.eu and have resulted in over 24,000 experiments undertaken by more than 1,800 students across 10 countries worldwide. This work has shown that the use of remote high- performance testbed facilities for hands-on remote experimentation can have a valuable impact on the learning experience for both educators and learners. Additionally, certain challenges in developing FIRE-based courseware have been identified, which has led to a set of recommendations in order to support the use of FIRE facilities for teaching and learning purposes