Page 75 - IJOCTA-15-1

BSO: Binary Sailfish Optimization for feature selection in sentiment analysis

[64] Sayyida, S., Hartini, S., Gunawan, S., & Husin, [79] Hammouti, I., Lajjam, A., Merouani, M., &
S.N. (2021). The impact of the COVID-19 pan- Tabaa, Y. (2019). A modified sailfish optimizer
demic on retail consumer behavior. Aptisi Trans- to solve dynamic berth allocation problem in
actions on Management (ATM), 5(1), 79–88. conventional container terminal. International
[65] Alexandropoulos, S.A.N., Kotsiantis, S.B., & Journal of Industrial Engineering Computations,
Vrahatis, M.N. (2019). Data preprocessing in pre- 10(4), 491–504.
dictive data mining. The Knowledge Engineering [80] Kumar, B.S., Santhi, S., & Narayana, S. (2022).
Review, 34, e1. Sailfish optimizer algorithm (SFO) for optimized
[66] Kumar, V., & Minz, S. (2014). Feature selection: clustering in wireless sensor network (WSN).
a literature review. SmartCR, 4(3), 211–229. Journal of Engineering, Design and Technology,
[67] Khalid, S., Khalil, T., & Nasreen, S. (2014). A 20(6), 1449–1467.
survey of feature selection and feature extraction [81] Li, M., Li, Y., Chen, Y., & Xu, Y. (2021).
techniques in machine learning. 2014 Science and Batch recommendation of experts to questions in
Information Conference, 372–378. community-based question-answering with a sail-
[68] Vijayarani, S., Ilamathi, M.J., & Nithya, M. fish optimizer. Expert Systems with Applications,
(2015). Preprocessing techniques for text mining- 169, 114484.
an overview. International Journal of Computer [82] Ghosh, K.K., Ahmed, S., Singh, P.K., Geem,
Science & Communication Networks, 5(1), 7–16. Z.W., & Sarkar, R. (2020). Improved bi-
[69] Nayak, A.S., Kanive, A.P., Chandavekar, N., nary sailfish optimizer based on adaptive β-hill
& Balasubramani, R. (2016). Survey on pre- climbing for feature selection. IEEE Access, 8,
processing techniques for text mining. Interna- 83548–83560.
tional Journal of Engineering and Computer Sci- [83] Siami-Namini, S., Tavakoli, N., & Namin, A.S.
ence, 5(6), 16875–16879. (2019). The performance of LSTM and BiLSTM
[70] Silva, C., & Ribeiro, B. (2003). The importance in forecasting time series. Proceedings of the 2019
of stop word removal on recall values in text cate- IEEE International Conference on Big Data (Big
gorization. Proceedings of the International Joint Data), 3285–3292.
Conference on Neural Networks, 3, 1661–1666. [84] Huang, Y., Jiang, Y., Hasan, T., Jiang, Q., &
[71] Kaur, J., & Buttar, P.K. (2018). A systematic Li, C. (2018). A topic BiLSTM model for senti-
review on stopword removal algorithms. Inter- ment classification. Proceedings of the 2nd Inter-
national Journal on Future Revolution in Com- national Conference on Innovation in Artificial
puter Science & Communication Engineering, Intelligence, 143–147.
4(4), 207–210. [85] Zhang, Y., & Rao, Z. (2020). n-BiLSTM: BiL-
[72] Bird, S., Klein, E., & Loper, E. (2009). Natural STM with n-gram features for text classifica-
language processing with Python: analyzing text tion. 2020 IEEE 5th Information Technology and
with the natural language toolkit. O’Reilly Media, Mechatronics Engineering Conference (ITOEC),
Inc. 1056–1059.
[73] Jivani, A.G. (2011). A comparative study of [86] Hameed, Z., & Garcia-Zapirain, B. (2020). Senti-
stemming algorithms. International Journal of ment classification using a single-layered BiLSTM
Computer Technology and Applications, 2(6), model. IEEE Access, 8, 73992–74001.
1930–1938. [87] Dwarampudi, M., & Reddy, N.V. (2019). Effects
[74] Lovins, J.B. (1968). Development of a stemming of padding on LSTMs and CNNs. arXiv preprint
algorithm. Mechanical Translation and Computa- arXiv:1903.07288.
tional Linguistics, 11(1-2), 22–31. [88] Liashchynskyi, P., & Liashchynskyi, P. (2019).
[75] M.F. Porter. (2001). Snowball: A language for Grid search, random search, genetic algorithm:
stemming algorithms [online]. Available from: ht a big comparison for NAS. arXiv preprint
tp://snowball.tartarus.org/texts/introd arXiv:1912.06059.
uction.html [Accessed 04-July-2023]. [89] Yang, X.S. (2009). Harmony search as a
[76] Krovetz, R. (1993). Viewing morphology as an metaheuristic algorithm. Music-inspired harmony
inference process. Proceedings of the 16th An- search algorithm: theory and applications, 1–14.
nual International ACM SIGIR Conference on [90] Yang, X.-S. (2010). A new metaheuristic bat-
Research and Development in Information Re- inspired algorithm. Nature inspired cooperative
trieval, 191–202. strategies for optimization (NICSO 2010), 65–74.
[77] Ramos, J. (2003). Using tf-idf to determine word [91] Ali, E. (2014). Optimization of power system
relevance in document queries. Proceedings of stabilizers using BAT search algorithm. Interna-
the First Instructional Conference on Machine tional Journal of Electrical Power & Energy Sys-
Learning, 242(1), 29–48. tems, 61, 683–690.
[78] Zhang, Y., & Mo, Y. (2021). Dynamic optimiza- [92] Geem, Z.W., Tseng, C.L., & Park, Y. (2005).
tion of chemical processes based on modified sail- Harmony search for generalized orienteering prob-
fish optimizer combined with an equal division lem: best touring in China. Proceedings of the In-
method. Processes, 9(10), 1806. ternational Conference on Natural Computation,
741–750.
69