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

Classification of Signals01:30

Classification of Signals

381
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...
381
Valence Bond Theory02:45

Valence Bond Theory

31.8K
Overview of Valence Bond Theory
31.8K
Stereotype Content Model02:16

Stereotype Content Model

14.0K
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...
14.0K
Attitudes01:54

Attitudes

28.1K
Attitude is our evaluation of a person, an idea, or an object. We have attitudes for many things ranging from products that we might pick up in the supermarket to people around the world to political policies. Typically, attitudes are favorable or unfavorable: positive or negative (Eagly & Chaiken, 1993). And, they have three components: an affective component (feelings), a behavioral component (the effect of the attitude on behavior), and a cognitive component (belief and knowledge;...
28.1K
Aggregates Classification01:29

Aggregates Classification

299
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...
299
Classification of Systems-I01:26

Classification of Systems-I

168
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
168

You might also read

Related Articles

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

Sort by
Same author

Relief of autoinhibition by conformational switch explains enzyme activation by a catalytically dead paralog.

eLife·2016
Same author

Ruthenium/Graphene-like Layered Carbon Composite as an Efficient Hydrogen Evolution Reaction Electrocatalyst.

ACS applied materials & interfaces·2016
Same author

Reduced graphene oxide for fiber-optic toluene gas sensing.

Optics express·2016
Same author

AntimiR-30b Inhibits TNF-α Mediated Apoptosis and Attenuated Cartilage Degradation through Enhancing Autophagy.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology·2016
Same author

Noncontact Synergistic Effect between Au Nanoparticles and the Fe<sub>2</sub>O<sub>3</sub> Spindle Inside a Mesoporous Silica Shell as Studied by the Fenton-like Reaction.

Langmuir : the ACS journal of surfaces and colloids·2016
Same author

An elaborate landscape of the human antibody repertoire against enterovirus 71 infection is revealed by phage display screening and deep sequencing.

mAbs·2016
Same journal

A tri-axis optomechanical accelerometer with plasmonic MIM waveguide and structural direction-dependent optical signatures.

Scientific reports·2026
Same journal

Holographic leaky-wave antennas with independently controlled multiple counter-rotating vortex beams.

Scientific reports·2026
Same journal

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026
Same journal

Abdominal obesity and leisure-time sedentary behavior in relation to gastroesophageal reflux disease risk: a prospective cohort study from the UK Biobank.

Scientific reports·2026
Same journal

Effect of nitrogen-rich COF incorporation on the structure and separation performance of polyamide nanofiltration membranes.

Scientific reports·2026
Same journal

Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: May 30, 2025

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

2.6K

Aspect category sentiment analysis based on pre-trained BiLSTM and syntax-aware graph attention network.

Guixian Xu1,2, Zhe Chen3,4, Zixin Zhang3,4

  • 1Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China. guixian_xu@muc.edu.cn.

Scientific Reports
|January 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for Aspect Category Sentiment Analysis (ACSA) using Bidirectional Long Short-Term Memory (BiLSTM) and syntax-aware graph attention networks. The method improves accuracy by better matching sentiment words to aspect categories, outperforming existing models.

Keywords:
Aspect category detectionAspect category sentiment analysisBidirectional long-short term memory networksGraph attention network

More Related Videos

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.5K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.6K

Related Experiment Videos

Last Updated: May 30, 2025

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

2.6K
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.5K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.6K

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Aspect Category Sentiment Analysis (ACSA) aims to identify sentiment towards specific categories within text.
  • Existing ACSA methods struggle with accurately associating sentiment words to aspect categories, especially when semantic relevance is indirect.
  • A significant challenge in ACSA is the scarcity of annotated datasets, hindering model training and performance.

Purpose of the Study:

  • To propose a novel and effective approach for Aspect Category Sentiment Analysis (ACSA).
  • To address the limitations of existing ACSA methods in matching sentiment words with relevant aspect categories.
  • To overcome the challenge of insufficient annotated data by employing transfer learning.

Main Methods:

  • Utilized a pre-trained Bidirectional Long Short-Term Memory (BiLSTM) model for initial sentiment analysis on a document-level dataset.
  • Implemented a syntax-aware graph attention network to leverage syntactic structure and semantic information for fine-grained sentiment prediction.
  • Employed transfer learning by transferring pre-trained BiLSTM parameters to the aspect-level ACSA model.

Main Results:

  • The proposed method demonstrated superior performance compared to baseline models across five user comment text datasets.
  • Comprehensive ablation experiments validated the effectiveness of the combined BiLSTM and syntax-aware graph attention network approach.
  • The integration of pre-trained knowledge and syntactic analysis significantly improved ACSA task achievement.

Conclusions:

  • The novel ACSA approach effectively addresses the challenge of matching sentiment words to aspect categories.
  • Transfer learning combined with a syntax-aware graph attention network provides a robust solution for ACSA, even with limited annotated data.
  • The proposed method represents a significant advancement in fine-grained sentiment analysis, offering improved accuracy and reliability.