Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights
Penulis/Author
PULUNG HENDRO P (1); Prof. Dr. Ir. Risanuri Hidayat, M.Sc., IPM. (2); Dr.Eng. Ir. Igi Ardiyanto, S.T., M.Eng. (3)
Tanggal/Date
2021
Kata Kunci/Keyword
Abstrak/Abstract
Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper proposes a new feature selection using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights. The proposed method was validated using five tweet datasets on different topics both in Indonesian and English, and compared with state-of-the-art of filter and wrapper-based feature selection methods. Experimental results show the proposed method significantly improves sentiment classification performance and decrease computational time.
Rumpun Ilmu
Teknik Elektro
Bahasa Asli/Original Language
English
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
Similarity Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm.pdf
