  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.
