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Related Experiment Video

Updated: Oct 2, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network.

Şafak Kayıkçı1

  • 1Department of Computer Engineering, Bolu Abant Izzet Baysal University, BAİBÜ Gölköy Yerleşkesi, 14030 Merkez/Bolu, Turkey.

Multimedia Tools and Applications
|February 23, 2022
PubMed
Summary

This study introduces SenDemonNet, a novel sentiment analysis model for understanding public opinion on India's demonetization policy. The model achieves superior classification accuracy by optimizing deep neural networks with a hybrid optimization algorithm.

Keywords:
Demonetization policyForest-whale optimization algorithmHeuristic deep neural networkSenDemonNetSentiment analysisTwitter dataWeighted feature selection

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Science

Background:

  • Sentiment analysis is crucial for extracting opinions from unstructured text data like social media.
  • Understanding public opinion on policy changes, such as demonetization, is vital for governance and analysis.
  • Twitter data offers a rich source for real-time public sentiment monitoring.

Purpose of the Study:

  • To develop and evaluate a novel sentiment analysis model, SenDemonNet, for assessing public opinion on India's demonetization policy.
  • To enhance classification accuracy in sentiment analysis through advanced feature selection and deep neural network optimization.

Main Methods:

  • Tweet preprocessing for text data cleaning.
  • Feature extraction using Bag of n-grams, TF-IDF, and word2vec.
  • Weighted feature selection via a hybrid Forest-Whale Optimization Algorithm (F-WOA).
  • Classification using a Heuristic Deep Neural Network (HDNN) optimized by F-WOA.

Main Results:

  • The proposed F-WOA-DNN model demonstrated superior performance, outperforming existing methods like PSO-DNN, GWO-DNN, WOA-DNN, FOA-DNN, SVM, CNN, LSTM, and DNN.
  • SenDemonNet achieved significant improvements in classification accuracy on benchmark datasets.
  • The hybrid F-WOA effectively optimized deep neural network parameters for enhanced sentiment classification.

Conclusions:

  • SenDemonNet offers a robust and accurate approach for sentiment analysis of policy-related discussions on Twitter.
  • The hybrid optimization algorithm (F-WOA) significantly boosts the performance of deep neural networks in sentiment classification tasks.
  • This research provides valuable insights into public perception of the demonetization policy through advanced computational methods.