PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Cao, D., Huang, Y., Li, H., Zhao, X., Chen, H., & Fu, Y. (2020, August 25-27). Text sentiment classification based on attention mechanism and decomposition convolutional neural network model. [Paper presentation]. IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China. https://doi.org/10.1109/AEECA49918.2020.9213672

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• Haddi, E., Liu, X., & Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26-32. https://doi.org/10.1016/j.procs.2013.05.005

• He, Y. (2023, July 14-16). BERT-CNN-BiLSTM: A Hybrid Deep Learning Model for Accurate Sentiment Analysis. [Paper presentation]. IEEE 5th International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China. https://doi.org/10.1109/ICPICS58376.2023.10235335

• Horakova, M. (2015). Sentiment analysis tool using machine learning. Global Journal on Technology, 2015(5), 195-204.

• Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University - Engineering Sciences, 30(4), 330-338. https://doi.org/10.1016/j.jksues.2016.04.002

• Krouska, A., Troussas, C., & Virvou, M. (2016, July 13-15). The effect of preprocessing techniques on Twitter sentiment analysis. [Paper presentation]. 7th International Conference on Information, Intelligence, Systems & Applications (IISA), Chalkidiki, Greece. https://doi.org/10.1109/IISA.2016.7785373

• Maree, M., & Eleyat, M. (2020). Semantic graph based term expansion for sentence-level sentiment analysis. International Journal of Computing 19(4), 647-655. https://doi.org/10.47839/ijc.19.4.2000

• Ni, R., & Cao, H. (2020, July 27-29). Sentiment analysis based on GloVe and LSTM-GRU. [Paper presentation]. 39th Chinese Control Conference (CCC), Shenyang, China. https://doi.org/10.23919/CCC50068.2020.9188578

• Qaisar, S. M. (2020, October 13-15). Sentiment analysis of IMDb movie reviews using long short-term memory. [Paper presentation]. 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia. https://doi.org/10.1109/ICCIS49240.2020.9257657

• Rusandi, M. R., Sutoyo, E., & Widartha, V. P. (2021, November 3-4). Convolutional neural network for predicting sentiment: Case study in tourism. [Paper presentation]. Sixth International Conference on Informatics and Computing (ICIC), Jakarta, Indonesia.

• Shaukat, Z., Zulfiqar, A. A., Xiao, C., Azeem, M., & Mahmood, T. (2020). Sentiment analysis on IMDB using lexicon and neural networks. SN Applied Sciences, 2(2), Article 148. https://doi.org/10.1007/s42452-019-1926-x

• Stojanovski, D., Strezoski, G., Madjarov, G., & Dimitrovski, I. (2015). Twitter sentiment analysis using deep convolutional neural network. In E. Onieva, I. Santos, E. Osaba, H. Quintian & E. Carchado (Eds.), Hybrid Artificial Intelligence Systems (pp. 726-737). Springer. https://doi.org/10.1007/978-3-319-19644-2_60

• Vielma, C., Verma, A., & Bein, D. (2020). Single and multibranch CNN-bidirectional LSTM for IMDb sentiment analysis. In S. Latifi. (Ed.), 17th International Conference on Information Technology–New Generations (pp. 401-406). Springer. https://doi.org/10.1007/978-3-030-43020-7_53

• Wang, M., Chen, S., & He, L. (2018). Sentiment classification using neural networks with sentiment centroids. In D. Phung, V. S. Tseng, G. O. Webb, B. Ho, M. Ganji & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 56-67). Springer. https://doi.org/10.1007/978-3-319-93034-3_5

• Wilson, T., Wiebe, J., & Hoffmann, P. (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35(3), 399-433. https://doi.org/10.1162/coli.08-012-R1-06-90

• Yang, P., & Chen, Y. (2017, December 15-17). A survey on sentiment analysis by using machine learning methods. [Paper presentation]. IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China. https://doi.org/10.1109/ITNEC.2017.8284920

• Yenter, A., & Verma, A. (2017, October 19-21). Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis. [Paper presentation]. IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York. USA. https://doi.org/10.1109/UEMCON.2017.8249013

• Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), Article e1253. https://doi.org/https://doi.org/10.1002/widm.1253

• Zhang, Y., & Wallace, B. (2015). A Sensitivity Analysis of (and practitioners’ guide to) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820. https://doi.org/10.48550/arXiv.1510.03820