Understanding Social Perception, Interactions, and Safety Aspects of Sidewalk Delivery Robots Using Sentiment Analysis

• URL: http://arxiv.org/abs/2405.00688v1
• Date: Sat, 9 Mar 2024 23:28:01 GMT
• Title: Understanding Social Perception, Interactions, and Safety Aspects of Sidewalk Delivery Robots Using Sentiment Analysis
• Authors: Yuchen Du, Tho V. Le,
• Abstract summary: This article presents a comprehensive sentiment analysis (SA) of comments on YouTube videos related to Sidewalk Delivery Robots (SDRs) We manually annotated the collected YouTube comments with three sentiment labels: negative (0), positive (1), and neutral (2). We then constructed models for text sentiment classification and tested the models' performance on both binary and ternary classification tasks.
• Score: 0.3069335774032178
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This article presents a comprehensive sentiment analysis (SA) of comments on YouTube videos related to Sidewalk Delivery Robots (SDRs). We manually annotated the collected YouTube comments with three sentiment labels: negative (0), positive (1), and neutral (2). We then constructed models for text sentiment classification and tested the models' performance on both binary and ternary classification tasks in terms of accuracy, precision, recall, and F1 score. Our results indicate that, in binary classification tasks, the Support Vector Machine (SVM) model using Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram get the highest accuracy. In ternary classification tasks, the model using Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory Networks (LSTM) and Gated Recurrent Unit (GRU) significantly outperforms other machine learning models, achieving an accuracy, precision, recall, and F1 score of 0.78. Additionally, we employ the Latent Dirichlet Allocation model to generate 10 topics from the comments to explore the public's underlying views on SDRs. Drawing from these findings, we propose targeted recommendations for shaping future policies concerning SDRs. This work provides valuable insights for stakeholders in the SDR sector regarding social perception, interaction, and safety.

Related papers

• AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders [73.37603699731329]
  We introduce AxBench, a large-scale benchmark for steering and concept detection. For steering, we find that prompting outperforms all existing methods, followed by finetuning. For concept detection, representation-based methods such as difference-in-means, perform the best.
  arXiv  Detail & Related papers  (2025-01-28T18:51:24Z)
• Testing Human-Hand Segmentation on In-Distribution and Out-of-Distribution Data in Human-Robot Interactions Using a Deep Ensemble Model [40.815678328617686]
  We present a novel approach by evaluating the performance of pre-trained deep learning models under both ID data and more challenging OOD scenarios. We incorporated unique and rare conditions such as finger-crossing gestures and motion blur from fast-moving hands. Results revealed that models trained on industrial datasets outperformed those trained on non-industrial datasets.
  arXiv  Detail & Related papers  (2025-01-13T21:52:46Z)
• ROSAR: An Adversarial Re-Training Framework for Robust Side-Scan Sonar Object Detection [0.3774866290142281]
  This paper introduces ROSAR, a framework enhancing the robustness of object detection models tailored for side-scan sonar (SSS) images. We introduce three novel, publicly available SSS datasets, capturing different sonar setups and noise conditions. ROSAR demonstrates significant improvements in model robustness and detection accuracy under SSS-specific conditions, enhancing the model's robustness by up to 1.85%.
  arXiv  Detail & Related papers  (2024-10-14T14:33:14Z)
• Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
  Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
  arXiv  Detail & Related papers  (2024-06-26T05:30:21Z)
• RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding [6.738409533239947]
  The Recap, Deliberate, and Respond (RDR) paradigm addresses this issue by incorporating three distinct objectives within the neural network pipeline. By cascading these three models, we mitigate the potential for gaming the benchmark and establish a robust method for capturing the underlying semantic patterns. Our results demonstrate improved performance compared to competitive baselines, with an enhancement of up to 2% on standard metrics.
  arXiv  Detail & Related papers  (2023-12-15T16:41:48Z)
• Helping Hands: An Object-Aware Ego-Centric Video Recognition Model [60.350851196619296]
  We introduce an object-aware decoder for improving the performance of ego-centric representations on ego-centric videos. We show that the model can act as a drop-in replacement for an ego-awareness video model to improve performance through visual-text grounding.
  arXiv  Detail & Related papers  (2023-08-15T17:58:11Z)
• Comparative Analysis of the wav2vec 2.0 Feature Extractor [42.18541127866435]
  We study the capability to replace the standard feature extraction methods in a connectionist temporal classification (CTC) ASR model. We show that both are competitive with traditional FEs on the LibriSpeech benchmark and analyze the effect of the individual components.
  arXiv  Detail & Related papers  (2023-08-08T14:29:35Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application [63.10266319378212]
  We propose a method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT) We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers.
  arXiv  Detail & Related papers  (2020-09-22T02:15:05Z)
• Understanding Road Layout from Videos as a Whole [82.30800791500869]
  We formulate it as a top-view road attributes prediction problem and our goal is to predict these attributes for each frame both accurately and consistently. We exploit the following three novel aspects: leveraging camera motions in videos, including context cuesand incorporating long-term video information.
  arXiv  Detail & Related papers  (2020-07-02T00:59:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.