DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion
Tirana Fatyanosa, Al Hafiz Akbar Maulana Siagian, Masayoshi Aritsugi
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- github.com/TiraNosa/Text-Classification-with-Scikit-LearnOfficialIn papernone★ 0
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Abstract
This paper describes the participation of DBMS-KU team in the SemEval 2019 Task 9, that is, suggestion mining from online reviews and forums. To deal with this task, we explore several machine learning approaches, i.e., Random Forest (RF), Logistic Regression (LR), Multinomial Naive Bayes (MNB), Linear Support Vector Classification (LSVC), Sublinear Support Vector Classification (SSVC), Convolutional Neural Network (CNN), and Variable Length Chromosome Genetic Algorithm-Naive Bayes (VLCGA-NB). Our system obtains reasonable results of F1-Score 0.47 and 0.37 on the evaluation data in Subtask A and Subtask B, respectively. In particular, our obtained results outperform the baseline in Subtask A. Interestingly, the results seem to show that our system could perform well in classifying Non-suggestion class.