Optimization of Feature Set for Sentiment Analysis using Artificial Butterfly Algorithm and Ensemble Machine Learning

Authors

  • Jyoti Hanvat M.Tech. Scholar Department of CSE VITS, Bhopal (M.P), India
  • Sumit Sharma Professor, Department of CSE, VITS, Bhopal (M.P), India

DOI:

https://doi.org/10.24113/ijoscience.v6i11.325

Keywords:

Deep neural networks, Aspect based sentiment analysis, Accuracy.

Abstract

The current decade has witnessed the remarkable developments in the field of artificial intelligence, and the revolution of deep learning has transformed the whole artificial intelligence industry. Eventually, deep learning techniques have become essential components of any model in today’s computational world. Nevertheless, ensemble learning techniques promise a high degree of automation with generalized rule extraction for both text and sentiment classification tasks. This paper aims designed and implemented optimized feature matrix using ensemble learning used for sentiment classification and its applications.

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Published

11/25/2020

How to Cite

Hanvat, J. ., & Sharma, S. . (2020). Optimization of Feature Set for Sentiment Analysis using Artificial Butterfly Algorithm and Ensemble Machine Learning . SMART MOVES JOURNAL IJOSCIENCE, 6(11), 21–27. https://doi.org/10.24113/ijoscience.v6i11.325