A Comparative Study of Evolutionary Methods for Feature Selection in Sentiment Analysis

Shikhar Garg, Sukriti Verma

2019

Abstract

With the recent surge of social media and other forums, availability of a large volume of data has rendered sentiment analysis an important area of research. Though current state-of-the-art systems have been demonstrated impressive performance, there is still no consensus on the optimum feature selection algorithm for the task of sentiment analysis. Feature selection is an indispensable part of the pipeline in natural language models as the data in this domain has extremely high dimensionality. In this work, we investigate the performance of two meta-heuristic feature selection algorithms namely Binary Bat and Binary Grey Wolf. We compare the results obtained to employing Genetic Algorithm for the same task. We report the results of our experiments on publicly available datasets drawn from two different domains, viz. tweets and movie reviews. We have used SVM, k-NN and Random Forest as the classification algorithms.

Paper Citation

in Harvard Style

Garg S. and Verma S. (2019). A Comparative Study of Evolutionary Methods for Feature Selection in Sentiment Analysis. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA; ISBN 978-989-758-384-1, SciTePress, pages 131-138. DOI: 10.5220/0007948201310138

in Bibtex Style

@conference{ecta19,
author={Shikhar Garg and Sukriti Verma},
title={A Comparative Study of Evolutionary Methods for Feature Selection in Sentiment Analysis},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA},
year={2019},
pages={131-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007948201310138},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA
TI - A Comparative Study of Evolutionary Methods for Feature Selection in Sentiment Analysis
SN - 978-989-758-384-1
AU - Garg S.
AU - Verma S.
PY - 2019
SP - 131
EP - 138
DO - 10.5220/0007948201310138
PB - SciTePress