Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm

Abstract

Traditional urban planning processes typically happen in offices and behind desks. Modern types of civic participation can enhance those processes by acquiring citizens' ideas and feedback in participatory sensing approaches like "People as Sensors". As such, citizen-centric planning can be achieved by analysing Volunteered Geographic Information (VGI) data such as Twitter tweets and posts from other social media channels. These user-generated data comprise several information dimensions, such as spatial and temporal information, and textual content. However, in previous research, these dimensions were generally examined separately in single-disciplinary approaches, which does not allow for holistic conclusions in urban planning. This paper introduces TwEmLab, an interdisciplinary approach towards extracting citizens' emotions in different locations within a city. More concretely, we analyse tweets in three dimensions (space, time, and linguistics), based on similarities between each pair of tweets as defined by a specific set of functional relationships in each dimension. We use a graph-based semi-supervised learning algorithm to classify the data into discrete emotions (happiness, sadness, fear, anger/disgust, none). Our proposed solution allows tweets to be classified into emotion classes in a multi-parametric approach. Additionally, we created a manually annotated gold standard that can be used to evaluate TwEmLab's performance. Our experimental results show that we are able to identify tweets carrying emotions and that our approach bears extensive potential to reveal new insights into citizens' perceptions of the city.

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

“…Consequently, our results serve as a basis for further, more in-depth analyses. In general, both previous research and the work presented in this article have shown that results from social media analysis are directly usable in urban planning processes, including the general ability to detect sentiments that are associated with places (Resch et al, 2016). In this regard, social media provide people (local citizens and visitors) with a simple and powerful instrument to share their opinions and subjective impressions.…”

“…This openness is of particular importance because urban planning processes are oftentimes still characterized by closed communication between local and official actors, lacking open discussion and transparent procedures (Resch et al, 2016). Moreover, openness and transparency are increasingly a key factor for successful urban planning, allowing for an efficient weighing process that considers the opinions and sentiments of different stakeholders.…”

“…Girardin et al (2009) evaluated urban attractiveness by analyzing images from Flickr and mobile phone usage data, while Sun, Fan, Bakillah and Zipf (2015) used geo-tagged images for road-based travel recommendations. As another approach, several researchers improved and refined various methodologies for extracting emotions (Resch et al, 2016), transit rider satisfaction (Collins, Hasan, & Ukkusuri, 2013), and community happiness (Quercia et al, 2012) from Twitter data, also combined with demographics and other objective characteristics of a place such as education or obesity (Mitchell, Frank, Harris, Dodds, & Danforth, 2013), or even defined sentiment as a function of movement . The advantages of utilizing available additional datasets such as demographics, mobile phone data or mobility trajectories are twofold; they can help the interpretation of the primary results extracted from social media, and, on the other hand, they are also appropriate for validation purposes.…”

“…Another advantage of using these sources is the potential for extracting direct feedback about city life-related topics, places or phenomena by revealing subjective aspects as well, such as public mood or emotions Quercia, Ellis, Capra, & Crowcroft, 2012;Resch, Summa, Zeile, & Strube, 2016). This provides an opportunity to investigate what people actually think about parts of the city and the direct or indirect effects of a large event.…”

Classification of Traffic Related Short Texts to Analyse Road Problems in Urban Areas

“…Another example of VGI system is the US Environmental Protection Agency, a New York's department system where people provide and also collect information about the air and water pollution in their city (Resch et al, 2016).…”