Opinion Mining at Scale: A Case Study of the First Self-driving Car Fatality

Li, Tao; Choi, Mack Joong; Guo, Yuntao; Lin, Lei

doi:10.1109/bigdata.2018.8622211

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…However, the most objective outcomes of such analyses come from reaching as many people as possible and trying to include individuals with varying backgrounds, locations, and levels of interest in the user group. Social media mining and natural language processing offer an effective set of tools in this and have been used in a large number of applications such as stock market prediction [8], disaster relief [9], various stressful events [10], and autonomous cars [11]. Finally, even in the bounded field of social media natural language processing, there is a plethora of approaches that utilize various models, such as rule-based approaches [12], unsupervised methods [13], and attention models [14].…”

Section: Previous Workmentioning

confidence: 99%

“…Social media mining and natural language processing offer an effective set of tools in this that has been used in a large number of applications such as stock market prediction [8], disaster relief [9], various stressful events [10], and autonomous cars [11]. Specifically, [11] uses social media posts to mine the opinions towards fatalities with self-driving cars. This work focuses on the views of autonomous mobility before and after an accident that claimed the life of one pedestrian as described in [15].…”

Section: Relevant Previous Studiesmentioning

confidence: 99%

“…This work focuses on the views of autonomous mobility before and after an accident that claimed the life of one pedestrian as described in [15]. To assess the changes in views, [11] analyzed the comments under self-driving related videos in YouTube. The results can be viewed in two different word clouds, presented in Figure 1a for before the crash and (b) for after.…”

Section: Relevant Previous Studiesmentioning

confidence: 99%

“…Word clouds generated (a) before and (b) after the killing of a pedestrian by an autonomous Uber truck [11].…”

Section: Relevant Previous Studiesmentioning

confidence: 99%

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Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

2020

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The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.

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Section: Previous Workmentioning

confidence: 99%

Section: Relevant Previous Studiesmentioning

confidence: 99%

Section: Relevant Previous Studiesmentioning

confidence: 99%

“…Word clouds generated (a) before and (b) after the killing of a pedestrian by an autonomous Uber truck [11].…”

Section: Relevant Previous Studiesmentioning

confidence: 99%

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Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

2020

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“…The latter represents misdetection of an existing object or obstacles. In the context of autonomous vehicle safety, false negatives are significantly dangerous [21] according to the case studies on autonomous vehicle accidents [22]. Therefore, we would like to put more emphasis on the confidence in existence rather than nonexistence of objects, and this necessity results in an asymmetry in the importance of sensed events.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

2020

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Safety is a major concern for autonomous vehicle driving. The autonomous vehicles relying solely on ego-vehicle sensors have limitations in dealing with collisions. The risk can be reduced by communicating with other vehicles sharing sensed information. In this paper, we study a real-time multisource data fusion scheme based on Dempster–Shafer theory of evidence (DS) through cooperative vehicle-to-vehicle (V2V) communications. The classical DS can produce erroneous outputs when confidences are highly conflicting. In particular, a false negative error on object detection may lead to serious accidents. We propose a novel metric to measure distance between confidences on the collected data taking the asymmetry in the importance of sensed events into account. Based on the proposed distance, we consider the weighted DS combination in order to fuse multiple confidences. The proposed scheme is simple, and enables the vehicles to combine confidences in real-time. From real-world experiments, we show that it is feasible to lower the false negative rate by the proposed data fusion scheme, and to avoid accidents using the proposed collision warning system.

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Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Li²,

2018

2018 IEEE International Conference on Big Data (Big Data)

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Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting highlevel representations. Finally we point out related datasets for experiments and future research directions.

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Opinion Mining at Scale: A Case Study of the First Self-driving Car Fatality

Cited by 6 publications

References 26 publications

Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

Real-Time Inter-Vehicle Data Fusion Based on a New Metric for Evidence Distance in Autonomous Vehicle Systems

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

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