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

Aggregates Classification01:29

Aggregates Classification

398
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
398
Classification of Signals01:30

Classification of Signals

965
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
965
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

176
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
176
Histogram01:05

Histogram

14.9K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
14.9K
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Multiple Bar Graph01:07

Multiple Bar Graph

8.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.1K

You might also read

Related Articles

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

Sort by
Same author

Therapeutic Potential of Extracellular Vesicles Derived from Sheep Placenta in the Treatment of Skin Disorders.

ACS omega·2026
Same author

Sinomenine restrains the proliferation and hyperactivation of B lymphocytes partly by inhibiting interferon regulatory factor 5.

Journal of ethnopharmacology·2026
Same author

Surgery Reduces Cancer-specific Death in Elderly Right-Sided Colon Cancer: A SEER-Based Competing-Risk Analysis.

Cancer control : journal of the Moffitt Cancer Center·2026
Same author

Maximizing synergistic cobenefits of wetland conservation and restoration: A cost-effective integrated framework for urban sustainability.

Journal of environmental management·2026
Same author

Graphene oxide-enhanced dissolvable microneedles for transdermal delivery of alendronate: Augmented permeation and antibacterial activity.

Journal of biomaterials applications·2026
Same author

Mosquito salivary protein antibodies as efficient endpoints for vector control trials: A mechanistic comparison with clinical and entomological outcomes.

Infectious Disease Modelling·2026

Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

661

Graph convolutional networks with hierarchical multi-head attention for aspect-level sentiment classification.

Xiaowen Li1, Ran Lu1, Peiyu Liu1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, 250358 China.

The Journal of Supercomputing
|April 18, 2022
PubMed
Summary

This study introduces a novel hierarchical multi-head attention and graph convolutional network (MHAGCN) for aspect-level sentiment classification. The MHAGCN method effectively links aspect words with context, outperforming existing approaches in sentiment analysis.

Keywords:
Aspect-level sentiment classificationAttention mechanismDeep learningGraph convolutional network

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

536
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

4.2K

Related Experiment Videos

Last Updated: Sep 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

661
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

536
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

4.2K

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Aspect-level sentiment classification is a fine-grained task crucial for understanding opinions on specific entities or attributes within text.
  • Graph convolutional networks (GCNs) have shown promise by leveraging sentence structure, but often fail to adequately connect aspect words with their surrounding context.
  • Existing methods struggle to fully integrate syntactic dependencies and semantic information for robust aspect-based sentiment analysis.

Purpose of the Study:

  • To propose a novel method, the hierarchical multi-head attention and graph convolutional network (MHAGCN), to improve aspect-level sentiment classification.
  • To enhance the interaction between aspect words and contextual information by considering syntactic dependencies and semantic features.
  • To address the limitations of previous methods in effectively linking aspect terms with their relevant context.

Main Methods:

  • Development of a hierarchical multi-head attention mechanism to capture complex relationships within the sentence.
  • Integration of the attention mechanism with a graph convolutional network (GCN) to model syntactic dependencies.
  • Combining syntactic information with semantic features to create a more comprehensive representation for sentiment classification.

Main Results:

  • Extensive experiments were conducted on three benchmark datasets to validate the proposed MHAGCN method.
  • The results demonstrate that the MHAGCN approach significantly outperforms current state-of-the-art methods in aspect-level sentiment classification.
  • The method shows superior performance in accurately predicting sentiment polarity for specific aspect words.

Conclusions:

  • The proposed MHAGCN method effectively addresses the challenge of linking aspect words with context in sentiment analysis.
  • The integration of hierarchical multi-head attention and GCNs provides a powerful framework for fine-grained sentiment classification.
  • This approach offers a significant advancement in the field of aspect-level sentiment analysis.