Social media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys?

Abstract

(2020): Social media as passive geoparticipation in transportation planning-how effective are topic modeling & sentiment analysis in comparison with citizen surveys?, Geo-spatial Information Science,

VNLSTM-PoseNet: A novel deep ConvNet for real-time 6-DOF camera relocalization in urban streets

“…In recent years, with the development of deep learning, scholars have also begun to try to introduce deep learning into the field of image localization and have made a lot of progress (Brachmann et al 2016;Melekhov et al 2017;Behera et al 2020;Shukla et al 2018;Prins and Van Niekerk 2021;Lock and Pettit 2020;Kosowski et al 2020). (Shotton et al 2013) trained a random forest on RGB-D images and transformed the positioning problem into a problem of minimizing the energy function on the possible camera position assumptions.…”

AI Augmented Approach to Identify Shared Ideas from Large Format Public Consultation

“…In this case, the top 20 issues detected from public petitions concerned topics familiar to the public (e.g., freedom of religions) or related to important social events (e.g., gun control), whereas less familiar topics such as student visas and diversity of children health care issues (breastfeeding, abduction, baby cares) received less attentions from the public. Likewise, Lock and Pettit [37] employed LDA models to analyse online surveys in order to identify what factors influence customer satisfaction towards public transit systems. However, there are drawbacks to using LDA, as these mostly involve the significant effort required to fine-tune the model and the manual interpretation to select meaningful topics from automatically generated topic candidates.…”

“…To overcome some of these limitations, the two methods have been used in tandem to evaluate public satisfaction of large-scale infrastructure projects where the public satisfaction are measured with sentiment analysis and topic modelling is used to identify the causes of the different sentiments [25,37,[40][41][42]. At the same time, the performance of NLP has been benefiting from ongoing advances of deep learning techniques [43].…”

Geospatial big data for urban planning and urban management

“…The article Social Media as passive geo-participation in transportation planning -how effective are topic modelling & sentiment analysis in comparison with citizen surveys? by Lock and Pettit (2020) investigated the opportunities in using social media data (Tweets) to engage with citizens and customers about public transport performance, where a wide array of topics, sentiments, and relationships were extracted from social media data about the public transportation system in Sydney, Australia. The article Equity issues and the PeCUS index: An indirect analysis of community severance, by Lara and Rodrigues da Silva (2020), proposed an indicator, PeCUS, based on the OpenStreetMap data and human-involved scoring on a series of criteria, to assess the quality of Pedestrian Crossings and also an indirect assessment of community severance.…”