TY - GEN
T1 - Feature Selection using Variable Length Chromosome Genetic Algorithm for Sentiment Analysis
AU - Fatyanosa, Tirana Noor
AU - Bachtiar, Fitra A.
AU - Data, Mahendra
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Research in sentiment analysis often have very high features for the classification and it might affect the model's accuracy. In this paper, the Variable Length Chromosome Genetic Algorithm with Naïve Bayes (VLCGA-NB) is utilized to analyze the twitter sentiment. The tweets are preprocessed in several steps before using it in the algorithm. The preprocessing conducted to reduce the number of features. After the preprocessing performed, the features that produce a higher fitness value is selected. There are five classes: Very Positive, Positive, Neutral, Negative, and Very Negative to be classified. Comparison of NB and VLCGA-NB is conducted to show the models accuracy. The experiments show that VLCGA-NB produces the higher value for the Best F-Measure results of each sentiment, lower number of features (188 features), higher accuracy (75.2%), and higher fitness value (3.088953) than NB.
AB - Research in sentiment analysis often have very high features for the classification and it might affect the model's accuracy. In this paper, the Variable Length Chromosome Genetic Algorithm with Naïve Bayes (VLCGA-NB) is utilized to analyze the twitter sentiment. The tweets are preprocessed in several steps before using it in the algorithm. The preprocessing conducted to reduce the number of features. After the preprocessing performed, the features that produce a higher fitness value is selected. There are five classes: Very Positive, Positive, Neutral, Negative, and Very Negative to be classified. Comparison of NB and VLCGA-NB is conducted to show the models accuracy. The experiments show that VLCGA-NB produces the higher value for the Best F-Measure results of each sentiment, lower number of features (188 features), higher accuracy (75.2%), and higher fitness value (3.088953) than NB.
KW - feature selection
KW - Genetic Algorithm
KW - Naïve Bayes
KW - Twitter Sentiment Analysis
UR - https://www.scopus.com/pages/publications/85065220763
U2 - 10.1109/SIET.2018.8693190
DO - 10.1109/SIET.2018.8693190
M3 - Conference contribution
AN - SCOPUS:85065220763
T3 - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
SP - 27
EP - 32
BT - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018
Y2 - 10 November 2018 through 12 November 2018
ER -