Authors:
Shikhar Garg
and
Sukriti Verma
Affiliation:
Adobe Systems, Noida, Uttar Pradesh and India
Keyword(s):
Meta-heuristic, Feature Selection, Evolutionary Algorithm, Binary Bat, Binary Grey Wolf, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
;
Swarm/Collective Intelligence
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.