Open AccessA New Framework of Feature Selection Approach for Sentiment Analysis
Author(s) -
Mochmad Wahyudi,
Muhammad Zarlis,
Herman Mawengkang,
Syahril Efendi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012065
Subject(s) - sentiment analysis , feature selection , computer science , selection (genetic algorithm) , feature (linguistics) , quality (philosophy) , product (mathematics) , data mining , process (computing) , artificial intelligence , machine learning , data science , mathematics , philosophy , linguistics , geometry , epistemology , operating system
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.