Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stereotype Content Model02:16

Stereotype Content Model

13.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
13.9K
Observational Learning01:12

Observational Learning

98
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
98

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

What factors enhance students' achievement? A machine learning and interpretable methods approach.

PloS one·2025
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

462

A study of text classification algorithms for live-streaming e-commerce comments based on improved BERT model.

Rong Zhou1, Qing Shen2, Huafeng Kong2

  • 1Faculty of Business and Economics, University of Malaya, Kuala Lumpur, Malaysia.

Plos One
|April 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hierarchical BERT model for classifying e-commerce live stream comments. The model enhances accuracy and efficiency in analyzing these valuable, high-volume customer interactions.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

901
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.4K

Related Experiment Videos

Last Updated: May 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

462
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

901
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.4K

Area of Science:

  • Natural Language Processing
  • Machine Learning
  • E-commerce Analytics

Background:

  • E-commerce live streaming generates vast amounts of brief, diverse 'bullet comments'.
  • Analyzing these comments is crucial for understanding customer engagement and marketing effectiveness.
  • Existing methods struggle with the volume and complexity of bullet comment data.

Purpose of the Study:

  • To develop an improved model for classifying e-commerce bullet comments.
  • To enhance the accuracy and efficiency of bullet comment analysis.
  • To facilitate valuable information extraction for marketing purposes.

Main Methods:

  • Proposed an improved BERT model utilizing a hierarchical classification structure.
  • Trained a parent class BERT model for broad categorization.
  • Developed subclass BERT models for fine-grained classification within categories.

Main Results:

  • The hierarchical BERT model significantly improved classification accuracy.
  • The model demonstrated enhanced efficiency in processing large comment volumes.
  • Empirical evidence confirmed the model's effectiveness.

Conclusions:

  • The hierarchical BERT approach offers a robust solution for e-commerce bullet comment analysis.
  • This method aids in extracting actionable insights from live stream interactions.
  • Improved analysis supports more effective marketing strategies in e-commerce.