Meta Heuristic Optimization Algorithm for Twitter Data Sentiment Analysis

Authors

  • L. Sudha Rani Research Scholar, Dept.of CSE, JNTUA Anantapur, Andhra Pradesh, India, Asst.Professor G. Pulla Reddy Engineering College, Kurnool, Affiliated toJNTU Anantapur
  • S. Zahoor-Ul- Huq Professor, Dept. of CSE, G. Pulla Reddy Engineering College, Kurnool, AP, India
  • C. Shoba Bindu Professor, Dept.of CSE, JNTUA Anantapur, Andhra Pradesh, India

Keywords:

Twitter Sentiment Analysis, Optimization, Feature Selection, Multilayer Perception

Abstract

In recent years, sentiment classification in Twitter using deep learning approaches has gained popularity. Many researchers have focused on Twitter sentiment analysis and have assumed that all words within a tweet have the same polarity, often neglecting the polarity of individual words within the sentence. This paper proposes a novel approach to analyzing tweets, which consists of two main phases: feature selection and classification. In the first phase, the most appropriate features are selected through mutual information analysis. The second phase involves utilizing a Meta Heuristic algorithm to enhance the weights and biases of the multi-layer perceptron network. The study results demonstrate that the MLP network optimized by the Glow-worm Swarm optimization outperforms other existing methods.

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Published

25.12.2023

How to Cite

Rani, L. S. ., Huq, S. Z.-U.-., & Bindu, C. S. . (2023). Meta Heuristic Optimization Algorithm for Twitter Data Sentiment Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 312–318. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4254

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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