A New Framework of Feature Selection Approach for Sentiment Analysis. (November 2020)

• Record Type: Journal Article
• Authors: Wahyudi, Mochmad; Zarlis, Muhammad; Mawengkang, Herman; Efendi, Syahril
• Is Part Of: Journal of physics. Volume 1641(2020)
• Publication Date: 2020-11
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1641/1/012065
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Undoubtedly that the huge business data could make data analysis becomes more complicated such that the decision-making process would be out of reach. This condition happens. In the fields of consumer buying behavior, A well-known method called sentiment analysis can help in extracting information about the up-to-date trends and is able to increase market value of product through improving its quality. One of the approaches in solving the sentiment analysis is feature selection technique. However, this technique contains a combinatorial behavior and the analysis of the huge data can experience uncertainty parameter. This paper describes a framework for solving the sentiment analysis based on feature selection approach using a stochastic combinatorial programming.