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All Journal International Journal of Electrical and Computer Engineering
Desai, Tushar
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Computer Science & IT Electrical & Electronics Engineering

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An improved reptile search algorithm-based machine learning for sentiment analysis Sureja, Nitesh; Chaudhari, Nandini M.; Bhatt, Jalpa; Desai, Tushar; Parikh, Vruti; Panesar, Sonia; Sureja, Heli; Kharva, Jahnavi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp755-766

Abstract

The rapid growth of mobile technologies has transformed social media, making it crucial for expressing emotions and thoughts. When making significant decisions, businesses and governments can benefit from understanding public opinion. This information makes sentiment analysis vital for understanding public sentiment polarity. This study develops a hyper tuned deep learning model with swarm intelligence and many approaches for sentiment analysis. convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), CNN-LSTM, BERT-LSTM, and BERT-CNN are the six deep learning models of the sentiment analysis using deep learning with reinforced learning based on reptile search algorithm (SA-DLRLRSA) model. The reptile search algorithm, an enhanced swarm intelligence algorithm (SIA), optimizes deep learning model hyper parameters. Word2Vec word embedding is used to convert textual input sequences to representative embedding spaces. Pre-trained Word2Vec embedding is also used to address issue of unbalanced datasets. Experimental results demonstrate that the SA-DLRLRSA model works best with accuracies of 93.1%, 94.7%, 96.8%, 96.3%, 97.2%, and 98.3% utilizing CNN, LSTM, BERT, CNN-LSTM, BERT-CNN, and BERT-LSTM.
Co-Authors Bhatt, Jalpa Chaudhari, Nandini M. Kharva, Jahnavi Panesar, Sonia Parikh, Vruti Sureja, Heli Sureja, Nitesh