Jove
Visualize
Contact Us

Related Experiment Videos

IBNN: an integrated BERT-neural network framework for sentiment and emotion based text analysis.

Mrinmoy Kayal1,2, Jayadeep Pati3, Siddharth Kumar4

  • 1Department of Computer Science and Engineering, Indian Institute Of Information Technology Ranchi, Ranchi, Jharkhand, 834010, India. mrinmoy02.rs21@iiitranchi.ac.in.

Scientific Reports
|May 18, 2026
PubMed
Summary

Related Concept Videos

Empathy02:34

Empathy

Some researchers suggest that altruism operates on empathy. Empathy is the capacity to understand another person’s perspective, to feel what he or she feels. An empathetic person makes an emotional connection with others and feels compelled to help (Batson, 1991). Empathy can be expressed in several ways, including cognitive, affective, and motor.

You might also read

Related Articles

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

Sort by
Same author

Polypropylene wiping cloths contaminated with <i>S. aureus</i> and <i>P. aeruginosa</i> transfer significantly less to uncontaminated surfaces compared to microfiber and cotton.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2026
Same author

Enhancing Hindi OCR robustness with CRNN-ResNet50: a data augmentation approach on the devanagari dataset.

Scientific reports·2026
Same author

Numerical modeling of CZTS based heterostructured solar cell for high efficiency PV performance.

Scientific reports·2026
Same author

Protonic nickelate device networks for spatiotemporal neuromorphic computing.

Nature nanotechnology·2026
Same author

Evaluation of Patterns of Recurrences in Patients With Breast Carcinoma: Are We Treating the Right Volumes, or Does the Tumor Biology Play the Role?

Cureus·2026
Same author

Large tuning of the optical properties of nanoscale NdNiO<sub>3</sub> via electron doping.

Nanophotonics (Berlin, Germany)·2025
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

This study introduces the Integrated BERT with Neural Network (IBNN) model for advanced sentiment analysis (SA) on social media text. The IBNN model effectively predicts emotions by integrating Bidirectional Encoder Representations from Transformers (BERT) with neural networks.

Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Social Media Analytics

Background:

  • Increasing demand for text mining applications on social networks.
  • Abundant user comments on social media platforms offer valuable business insights.
  • Sentiment Analysis (SA) is crucial for understanding emotions in text data.

Purpose of the Study:

  • To compare the effectiveness of five feature extraction techniques (CountVectorizer, TF-IDF, Word2Vec, GloVe, BERT) for SA.
  • To evaluate machine learning, ensemble, and neural network algorithms on four emotion datasets.
  • To demonstrate the superior performance of the proposed Integrated BERT with Neural Network (IBNN) model.

Main Methods:

  • Development of the IBNN model integrating Bidirectional Encoder Representations from Transformers (BERT) with neural networks.
Keywords:
BERTIBNNMachine learningNeural networkSentiment analysis

Related Experiment Videos

  • Implementation of feature extraction techniques: CountVectorizer, TF-IDF Vectorizer, Word2Vec, GloVe, and BERT.
  • Comparative analysis using Decision Tree, Random Forest, XGBoost, Neural Network, and the IBNN model across four emotion datasets.

    • Main Results:

    • The IBNN model, integrating BERT with neural networks, achieved the highest performance across all four emotion datasets.
    • Comparative analysis highlighted the varying effectiveness of different feature extraction methods.
    • The study validated the efficacy of advanced NLP techniques for emotion detection in social media.

    Conclusions:

    • The proposed IBNN model offers a significant advancement in sentiment analysis accuracy.
    • Integrating BERT with neural networks provides a powerful approach for emotion prediction.
    • This research contributes valuable insights into analyzing large-scale social media text data for emotional states.