Authors:
Luís Marcello Moraes Silva
1
;
Carlos Roberto Valêncio
1
;
Geraldo Francisco Donegá Zafalon
1
and
Angelo Cesar Columbini
2
Affiliations:
1
Institute of Biosciences, São Paulo State University (Unesp), Humanities and Exact Sciences (Ibilce), Campus São José do Rio Preto, São Paulo, Brazil
;
2
Fluminense Federal University (UFF), Niterói, Rio de Janeiro, Brazil
Keyword(s):
Sentiment Analysis, Feature Selection, Cuckoo Search, Genetic Algorithm, Machine Learning, Social Media.
Abstract:
Social media sentiment analysis consists on extracting information from users’ comments. It can assist the decision-making process of companies, aid public health and security and even identify intentions and opinions about candidates in elections. However, such data come from an environment with big data characteristics, which can make traditional and manual analysis impracticable because of the high dimensionality. The implications on the analysis are high computational cost and low quality of results. Up to date research focuses on how to analyse feelings of users with machine learning and inspired by nature methods. To analyse such data effectively, a feature selection through cuckoo search and genetic algorithm is proposed. Machine learning with lexical analysis has become an attractive alternative to overcome this challenge. This paper aims to present a hybrid bio-inspired approach to realize feature selection and improve sentiment classification quality. The scientific contrib
ution is the improvement of a classification model considering pre-processing of the data with different languages and contexts. The results prove that the developed method enriches the predictive model. There is an improvement of around 13% in accuracy with a 45% average usage of attributes related to traditional analysis.
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