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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

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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.

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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.