Open Access

ARTICLE

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

R. Bhaskaran^1,*, S. Saravanan¹, M. Kavitha², C. Jeyalakshmi³, Seifedine Kadry⁴, Hafiz Tayyab Rauf⁵, Reem Alkhammash⁶

1 Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, 624622, India
2 Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Trichy, 621112, India
3 Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Tiruchirappalli, 621112, India
4 Deparmtent of Applied Data Science, Noroff University College, Kristiansand, Norway
5 Department of Computer Science, Faculty of Engineering & Informatics, University of Bradford, Bradford, United Kingdom
6 English Department, University College, Taraba, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: R. Bhaskaran. Email:

Computer Systems Science and Engineering 2023, 44(1), 235-247. https://doi.org/10.32604/csse.2023.024399

Received 15 October 2021; Accepted 29 December 2021; Issue published 01 June 2022

Abstract

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.

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Keywords

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