A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

BIM-based safety design for emergency evacuation of metro stations

Metro stations are the hubs of urban rail transit, and large numbers of people usually gather inside them. Various types of emergency can lead to a need for evacuation. However, there are few studies on proactively reducing emergency evacuation risks through the design for safety (DFS) concept, and these risks pose serious threats to the operational safety of metro stations. Therefore, in this research, fragmented DFS pre-control measures for mitigating emergency evacuation risks were comprehensively identified and classified, and indicators for evaluating the evacuation design effect on reducing emergency evacuation risks in the operation phase were improved. Moreover, through the combination of the DFS application method and BIM platform, intelligent safety design tools were provided for metro station designers so that they may apply the DFS concept to emergency evacuation risk mitigation in real cases

Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping

Correlated time series analysis plays an important role in many real-world industries. Learning an efficient representation of this large-scale data for further downstream tasks is necessary but challenging. In this paper, we propose a time-step-level representation learning framework for individual instances via bootstrapped spatiotemporal representation prediction. We evaluated the effectiveness and flexibility of our representation learning framework on correlated time series forecasting and cold-start transferring the forecasting model to new instances with limited data. A linear regression model trained on top of the learned representations demonstrates our model performs best in most cases. Especially compared to representation learning models, we reduce the RMSE, MAE, and MAPE by 37%, 49%, and 48% on the PeMS-BAY dataset, respectively. Furthermore, in real-world metro passenger flow data, our framework demonstrates the ability to transfer to infer future information of new cold-start instances, with gains of 15%, 19%, and 18%. The source code will be released under the GitHub https://github.com/bonaldli/Spatiotemporal-TS-Representation-LearningComment: Accepted to IEEE CASE 202

Bike-sharing: the good, the bad, and the future -an analysis of the public discussion on Twitter-

Due to the dilemma of bike-sharing concerning its benefits and drawbacks, and its unclear future, we focused on a mixed-methods approach to analyze this public discussion through posts or “tweets” from the social media channel Twitter. We collected around 12,000 tweets in English around the world related to bike-sharing for a period of about six months. We considered two approaches, including topic clustering and sentiment analysis in tweets including: a) bike-sharing related terms and b) “future” and bike-sharing related terms. Strongly positive tweets promote bike-sharing and its benefits such as being convenient, well-performing, and sustainable. Additionally, there is a tendency to write that public, electric, and dockless are better, together with scooters. In contrast, the complaints on bike-sharing focused on inequity, rentals and safety issues, critique on authorities and laws, and poor performance especially of dockless Asian bike-sharing start-ups with low-quality bikes. Around 50% of the tweets that included the terms “future” and “bike–sharing” stated that bike-sharing is going to be part of the future of mobility as an electric dockless version together with other shared modes. The hesitant statements towards bike-sharing being part of the future referred mainly to the systems with poor bikes’ quality. Politicians and stakeholders can use this information to enhance bike-sharing or consider the implementation of certain types of bike-sharing in their cities. To the best of the authors’ knowledge, this study would be one of the first that analysis the public discussion on social media about a transportation system and its future using a mixed-methods approach. Future studies should aim at identifying and comparing the public opinion of different emerging transportation technologies

A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges