Understanding Social Perception, Interactions, and Safety Aspects of Sidewalk Delivery Robots Using Sentiment Analysis
Yuchen Du, Tho V. Le

TL;DR
This study analyzes YouTube comments on Sidewalk Delivery Robots using sentiment analysis, machine learning models, and topic modeling to understand public perception and inform policy recommendations.
Contribution
It introduces a comprehensive sentiment classification framework and topic modeling approach to assess social perception of SDRs from online comments.
Findings
Support Vector Machine with TF-IDF and N-grams achieves highest binary classification accuracy.
BERT, LSTM, and GRU models outperform others in ternary sentiment classification.
Latent Dirichlet Allocation reveals 10 key topics reflecting public views on SDRs.
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,…
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Taxonomy
TopicsSocial Robot Interaction and HRI · Evacuation and Crowd Dynamics
