Twitter Data Sentiment Analysis using a Novel Pairing Scheme of GHO

Twitter Data Sentiment Analysis using a Novel Pairing Scheme of GHO
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© 2024 by IJETT Journal
Volume-72 Issue-11
Year of Publication : 2024
Author : Neha Sharma, Sanjay Tyagi
DOI : 10.14445/22315381/IJETT-V72I11P132

How to Cite?
Neha Sharma, Sanjay Tyagi, "Twitter Data Sentiment Analysis using a Novel Pairing Scheme of GHO," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 333-344, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P132

Abstract
In social media, numerous public networks, namely Twitter, Facebook, YouTube, Instagram, etc., are used to communicate through videotapes, pictures, and posts. Adapting to such information and data mining from such websites will become troublesome in the future. Sentimental analysis, a type of contextual mining, is frequently popular on social media and includes machine learning-based, lexicon-based, and hybrid methods. This paper aims to develop sentimental analysis using a novel optimization technique pairing scheme. The methodology includes the Grasshopper Optimization Algorithm (GOA) devised for sentimental analysis using the Twitter dataset and the Amazon Reviews dataset for sentimental analysis. The proposed method dealt with the pre-processing of the dataset and evaluation of features using different similarity metrics such as Term Frequency (tf), Inverse Document Frequency (idf), Tanimoto Co-efficient, and Cosine similarity. The optimized and extracted features are further classified using the Machine Learning tool for both datasets. The developed technique provides better accuracy, 91.65% for the Twitter dataset and 88.4% for the Amazon Reviews dataset. The results were further compared with the existing techniques for superiority.

Keywords
Sentimental analysis, Twitter, Data mining, Grasshopper, Machine learning.

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