Abstract
Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages--leading to biased machine-learning--and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel fine-tuned BERT language model and aspect-based sentiment analysis. BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail. Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection. Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions. Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches.
Highlights
JOSIS, as Mike Worboys put it in his editorial introducing the first issue ten years ago, is “an online publication and all articles are free to access for any person” [17]
We asked all members of our editorial board to write vision pieces showing the diversity of ways in which our field can contribute to both basic science and major societal challenges
Some editorial board members chose to write their piece alone; others asked colleagues to contribute. These are diverse, and they span a range of topics
Summary
JOSIS, as Mike Worboys put it in his editorial introducing the first issue ten years ago, is “an online publication and all articles are free to access for any person” [17]. Climate change has become a climate emergency, biodiversity and nature’s contributions to people have been recognized as important contributors to Sustainable Development Goals1, easy access to locationenabled devices in many people’s pockets has changed the way that societies operate, and most recently, a global pandemic has impacted on all of our lives. All of these events bring into sharp focus the ways in which the data, technologies, and methods we work on can be used for good.
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