K. X. Han, W. Chien, C. C. Chiu, and Y. T. Cheng, “Application of support vector machine (SVM) in the sentiment analysis of twitter dataset,” Appl. Sci., vol. 10, no. 3, 2020, doi: 10.3390/app10031125.
V. Nurcahyawati and Z. Mustaffa, “Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm,” Bull. Electr. Eng. Informatics, vol. 12, no. 3, pp. 1817–1824, 2023, doi: 10.11591/eei.v12i3.4830.
K. Tamara and N. Milićević, “Comparing Sentiment Analysis and Document Representation Methods of Amazon Reviews,” SISY 2018 - IEEE 16th Int. Symp. Intell. Syst. Informatics, Proc., pp. 283–288, 2018, doi: 10.1109/SISY.2018.8524814.
H. Zou, X. Tang, B. Xie, and B. Liu, “Sentiment classification using machine learning techniques with syntax features,” Proc. - 2015 Int. Conf. Comput. Sci. Comput. Intell. CSCI 2015, pp. 175–176, 2016, doi: 10.1109/CSCI.2015.44.
D. Dangi, A. Bhagat, and D. K. Dixit, “Sentiment analysis of social media data based on chaotic coyote optimization algorithm based time weight-AdaBoost support vector machine approach,” Concurrency and Computation: Practice and Experience, vol. 34, no. 3. 2022. doi: 10.1002/cpe.6581.
A. Muhammad, S. Abdullah, and N. S. Sani, “Optimization of sentiment analysis using teaching-learning based algorithm,” Comput. Mater. Contin., vol. 69, no. 2, pp. 1783–1799, 2021, doi: 10.32604/cmc.2021.018593.
C. Shofiya and S. Abidi, “Sentiment analysis on covid-19-related social distancing in Canada using twitter data,” Int. J. Environ. Res. Public Health, vol. 18, no. 11, 2021, doi: 10.3390/ijerph18115993.
F. Resyanto, Y. Sibaroni, and A. Romadhony, “Choosing The Most Optimum Text Preprocessing Method for Sentiment Analysis: Case:iPhone Tweets,” Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019, pp. 2–6, 2019, doi: 10.1109/ICIC47613.2019.8985943.
S. Rani, N. Singh Gill, and P. Gulia, “Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool,” Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 2, p. 1041, 2021, doi: 10.11591/ijeecs.v22.i2.pp1041-1051.
Y. Handayani, A. R. Hakim, and Muljono, “Sentiment analysis of Bank BNI user comments using the support vector machine method,” Proc. - 2020 Int. Semin. Appl. Technol. Inf. Commun. IT Challenges Sustain. Scalability, Secur. Age Digit. Disruption, iSemantic 2020, pp. 202–207, 2020, doi: 10.1109/iSemantic50169.2020.9234230.
B. AlBadani, R. Shi, and J. Dong, “A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM,” Appl. Syst. Innov., vol. 5, no. 1, 2022, doi: 10.3390/asi5010013.
N. V. Babu and E. G. M. Kanaga, “Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review,” SN Comput. Sci., vol. 3, no. 1, pp. 1–20, 2022, doi: 10.1007/s42979-021-00958-1.
B. T. Pratama, E. Utami, and A. Sunyoto, “An optimization of a lexicon based sentiment analysis method on Indonesian app review,” 2019 4th Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2019, pp. 341–346, 2019, doi: 10.1109/ICITISEE48480.2019.9003900.
B. T. Pratama, E. Utami, and A. Sunyoto, “A comparison of the use of several different resources on lexicon based Indonesian sentiment analysis on app review dataset,” Proceeding - 2019 Int. Conf. Artif. Intell. Inf. Technol. ICAIIT 2019, pp. 282–287, 2019, doi: 10.1109/ICAIIT.2019.8834531.
Y. Barve, J. R. Saini, K. Pal, and K. Kotecha, “A Novel Evolving Sentimental Bag-of-Words Approach for Feature Extraction to Detect Misinformation,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 4, pp. 266–275, 2022, doi: 10.14569/IJACSA.2022.0130431.
P. Verma, A. Dumka, A. Bhardwaj, and A. Ashok, “Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA),” Int. J. Appl. Metaheuristic Comput., vol. 13, no. 1, pp. 1–21, 2022, doi: 10.4018/ijamc.2022010107.
M. B. Ressan and R. F. Hassan, “Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets,” Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 1, pp. 375–383, 2022, doi: 10.11591/ijeecs.v28.i1.pp375-383.
F. Rustam, I. Ashraf, A. Mehmood, S. Ullah, and G. S. Choi, “Tweets classification on the base of sentiments for US airline companies,” Entropy, vol. 21, no. 11, pp. 1–22, 2019, doi: 10.3390/e21111078.
C. H. Yutika, A. Adiwijaya, and S. Al Faraby, “Analisis Sentimen Berbasis Aspek pada Review Female Daily Menggunakan TF-IDF dan Naïve Bayes,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 422, 2021, doi: 10.30865/mib.v5i2.2845.
R. Obiedat et al., “Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution,” IEEE Access, vol. 10, pp. 22260–22273, 2022, doi: 10.1109/ACCESS.2022.3149482.
P. H. Prastyo, A. S. Sumi, A. W. Dian, and A. E. Permanasari, “Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel,” J. Inf. Syst. Eng. Bus. Intell., vol. 6, no. 2, p. 112, 2020, doi: 10.20473/jisebi.6.2.112-122.