Jove
Visualize
Contact Us

Related Concept Videos

Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

7.5K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
7.5K
Stereotype Content Model02:16

Stereotype Content Model

15.0K
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...
15.0K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

82
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
82
Force Classification01:22

Force Classification

1.9K
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,...
1.9K
Aggregates Classification01:29

Aggregates Classification

433
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...
433
Classification of Signals01:30

Classification of Signals

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Evaluating a virtual simulation chatbot to improve communication for radiation therapy students: A pilot study.

Journal of medical imaging and radiation sciences·2025
Same author

Acceptance and use behaviour of emerging technology for middle-aged healthy lifestyle.

Technology and health care : official journal of the European Society for Engineering and Medicine·2023
Same author

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.

PeerJ. Computer science·2021
Same author

A tree-based multiclassification of breast tumor histopathology images through deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2021
Same author

Privacy and data protection in mobile cloud computing: A systematic mapping study.

PloS one·2020
Same author

Electronic Medical Records in Greece and Oman: A Professional's Evaluation of Structure and Value.

International journal of environmental research and public health·2018
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 Experiment Video

Updated: Nov 2, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.0K

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach.

Christopher Ifeanyi Eke1,2, Azah Anir Norman1, Liyana Shuib1

  • 1Faculty of Computer Science and Information Technology, Department of Information Systems, University of Malaya, Kuala Lumpur, Malaysia.

Plos One
|June 10, 2021
PubMed
Summary

Detecting sarcasm in tweets is challenging for natural language processing (NLP). A novel multi-feature fusion framework effectively identifies sarcasm by combining content and contextual information, achieving high precision.

Related Experiment Videos

Last Updated: Nov 2, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.0K

Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning
  • Sentiment Analysis

Background:

  • Sarcasm presents a significant challenge in accurately classifying tweet sentiment.
  • Existing methods often overlook contextual information, leading to semantic loss and data sparsity.
  • The word limit in microblogging contributes to sparse feature vectors using techniques like Bag-of-Words (BoW).

Purpose of the Study:

  • To propose a novel Multi-feature Fusion Framework for improved sarcasm detection in tweets.
  • To address the limitations of content-based features and data sparsity in existing approaches.
  • To enhance the accuracy of sentiment analysis by incorporating contextual information.

Main Methods:

  • A two-stage classification framework was developed.
  • Stage one utilized lexical features from Bag-of-Words (BoW) and five standard classifiers (SVM, DT, KNN, LR, RF) to predict sarcastic tendency.
  • Stage two fused the predicted sarcastic tendency with eight additional features to model context for final prediction.

Main Results:

  • The developed framework achieved a precision of 0.947 using a Random Forest classifier.
  • Experimental analysis demonstrated the effectiveness of the novel feature fusion approach.
  • The proposed framework significantly outperformed three baseline approaches.

Conclusions:

  • The Multi-feature Fusion Framework effectively overcomes the limitations of traditional sarcasm detection methods.
  • Integrating lexical and contextual features enhances the accuracy of sentiment analysis in NLP.
  • The study highlights the importance of comprehensive feature engineering for robust sarcasm detection.