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Optimized Support Vector Machine with Particle Swarm Optimization to Improve the Accuracy Amazon Sentiment Analysis Classification
Corresponding Author(s) : Jumanto Unjung
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 1, February 2024
Abstract
Text mining is a valuable technique that empowers users to gain a deeper understanding of existing textual data, ultimately allowing them to make more informed decisions. One important application of text mining is in the field of sentiment analysis, which has gained significant traction among companies aiming to understand how customers perceive their products and services. In response to this growing need, various research efforts have been made to improve the accuracy of sentiment analysis classification models. The purpose of this article is to discuss a specific approach using the Support Vector Machine (SVM) algorithm, which is often used in machine learning for text classification tasks and then combined with the application of Particle Swarm Optimization (PSO), which optimizes the SVM model parameters to achieve the best classification results. This dynamic combination not only improves accuracy but also enhances the model's ability to efficiently handle large amounts of text data to achieve better results. The research findings highlight the effectiveness of this approach. The application of the SVM algorithm with PSO resulted in an outstanding accuracy performance of 94.92%. The substantial increase in accuracy compared to previous studies shows the promising potential of this methodology. This proves that the SVM algorithm model approach with Particle Swarm Optimization provides good performance.
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- I. Castell-uroz, P. Barlet-ros, dan U. Polit, “Amazon Alexa traffic traces,” vol. 205, 2022. https://doi.org/10.1016/j.comnet.2022.108782
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- T. Singh dan M. Kumari, “Role of Text Pre-Processing in Twitter Sentiment Analysis,” Procedia - Procedia Comput. Sci., vol. 89, hal. 549–554, 2016. https://doi.org/10.1016/j.procs.2016.06.095
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References
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M. Shaden, A. Fadel, S. Achmad, dan R. Sutoyo, “Sentiment analysis for customer review : Case study of Traveloka Sentiment analysis for customer review : Case study of Traveloka,” Procedia Comput. Sci., vol. 216, no. 2022, hal. 682–690, 2023. https://doi.org/10.1016/j.procs.2022.12.184
C. Kim dan Y. Na, “Consumer reviews analysis on cycling pants in online shopping malls using text mining,” Fash. Text., 2021. https://doi.org/10.1186/s40691-021-00264-7
T. Nguyen, T. Ngoc, H. Nguyen, T. Thu, dan V. A. Nguyen, “Mining aspects of customer ’ s review on the social network,” J. Big Data, hal. 1–21, 2019. https://doi.org/10.1186/s40537-019-0184-5
L. Kong, C. Li, J. Ge, V. Ng, dan B. Luo, “Predicting Product Review Helpfulness – A Hybrid Method,” hal. 1–14, 2020. https://doi.org/10.1109/TSC.2020.3041095
V. A. Fitri, R. Andreswari, M. A. Hasibuan, V. A. Fitri, R. Andreswari, dan M. A. Hasibuan, “Sentiment Analysis of Social Media Twitter with Case of Anti- Sentiment Analysis of Social Media Twitter with Case of Anti- LGBT Campaign in Indonesia using Naïve Bayes , Decision Tree , LGBT Campaign in Indonesia using Naïve Bayes , Decision Tree , and R,” Procedia Comput. Sci., vol. 161, hal. 765–772, 2019. https://doi.org/10.1016/j.procs.2019.11.181
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J. Unjung dan M. R. Ningsih, “Optimized Handwriting-based Parkinson ’ s Disease Classification Using Ensemble Modeling and VGG19 Feature Extraction,” Sci. J. Informatics, vol. 10, no. 4, hal. 489–498, 2023. https://doi.org/10.15294/sji.v10i4.47108
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D. Asante, T. Omar, A. Ganat, R. Gholami, dan S. Ridha, “Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties : Comparative analysis of ANN and SVM models,” J. Pet. Sci. Eng., vol. 200, no. November 2020, hal. 108182, 2021. https://doi.org/10.1016/j.petrol.2020.108182
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A. Mee, E. Homapour, F. Chiclana, dan O. Engel, “Sentiment analysis using TF – IDF weighting of UK MPs ’ tweets on Brexit,” Knowledge-Based Syst., vol. 228, hal. 107238, 2021. https://doi.org/10.1016/j.knosys.2021.107238
J. Jumanto, M. A. Muslim, Y. Dasril, dan T. Mustaqim, “Accuracy of Malaysia Public Response to Economic Factors During the Covid-19 Pandemic Using Vader and Random,” J. Inf. Syst. Explor. Res., vol. 1, no. 1, hal. 49–70, 2023. https://doi.org/10.52465/joiser.v1i1.104
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M. R. Ningsih, K. Aalifian, H. Wibowo, dan A. U. Dullah, “Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding,” J. Soft Comput. Explor., vol. 4, no. 3, hal. 142–151, 2023. https://doi.org/10.52465/joscex.v4i3.193
R. A. Sinoara, “Text mining and semantics : a systematic mapping study,” 2017. https://doi.org/10.1186/s13173-017-0058-7
T. Singh dan M. Kumari, “Role of Text Pre-Processing in Twitter Sentiment Analysis,” Procedia - Procedia Comput. Sci., vol. 89, hal. 549–554, 2016. https://doi.org/10.1016/j.procs.2016.06.095
R. Rani dan D. K. Lobiyal, “Performance evaluation of text-mining models with Hindi stopwords lists,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, hal. 2771–2786, 2022. https://doi.org/10.1016/j.jksuci.2020.03.003
T. Hema et al., “Sentiment analysis of twitter data related to Rinca Island development using Doc2Vec and SVM and logistic regression as development using classifier,” Procedia Comput. Sci., vol. 197, no. 2021, hal. 660–667, 2022. https://doi.org/10.1016/j.procs.2021.12.187
R. Ahuja, A. Chug, S. Kohli, S. Gupta, dan P. Ahuja, “The Impact of Features Extraction on the Sentiment Analysis,” Procedia Comput. Sci., vol. 152, hal. 341–348, 2019. https://doi.org/10.1016/j.procs.2019.05.008
G. N. H, R. Siautama, A. C. I. A, dan D. Suhartono, “Extractive Hotel Review Summarization based on TF / IDF and Adjective-Noun Pairing by Considering Annual Sentiment Trends,” Procedia Comput. Sci., vol. 179, no. 2020, hal. 558–565, 2021. https://doi.org/10.1016/j.procs.2021.01.040
E. Gul, N. Alpaslan, dan M. E. Emiroglu, “Robust optimization of SVM hyper-parameters for spillway type selection,” Ain Shams Eng. J., vol. 12, no. 3, hal. 2413–2423, 2021. https://doi.org/10.1016/j.asej.2020.10.022
S. M. Malakouti, “Case Studies in Chemical and Environmental Engineering Improving the prediction of wind speed and power production of SCADA system with ensemble method and 10-fold cross-validation,” Case Stud. Chem. Environ. Eng., vol. 8, no. April, hal. 100351, 2023. https://doi.org/10.1016/j.cscee.2023.100351
K. F. Irnanda, A. P. Windarto, dan I. S. Damanik, “Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner,” J. Ris. Komput., vol. 9, no. 1, hal. 122–130, 2022. http://dx.doi.org/10.30865/jurikom.v9i1.3836
N. Hussain, H. T. Mirza, F. Iqbal, dan I. Memon, “Spam Review Detection Using the Linguistic and Spammer Behavioral Methods,” IEEE Access, vol. 8, hal. 53801–53816, 2020. https://doi.org/10.1109/ACCESS.2020.2979226
V. Ahuja dan M. Shakeel, “Twitter Presence of Jet Airways-Deriving Customer Insights Using Netnography and Wordclouds,” Procedia Comput. Sci., vol. 122, hal. 17–24, 2017. https://doi.org/10.1016/j.procs.2017.11.336
H. Cam, A. Veli, U. Demirel, dan S. Ahmed, “Heliyon Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers,” Heliyon, vol. 10, no. 1, hal. e23784, 2024. https://doi.org/10.1016/j.heliyon.2023.e23784
M. Isnan, G. Natanael, dan B. Pardamean, “Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model,” Procedia Comput. Sci., vol. 227, hal. 168–175, 2023. https://doi.org/10.1016/j.procs.2023.10.514
D. Wang dan Y. Zhao, “Using News to Predict on Investor Sentiment : on SVM and Knowledge Internet of Using News to Predict Investor Sentiment : Based on SVM Model,” Procedia Comput. Sci., vol. 174, no. 2019, hal. 191–199, 2020. https://doi.org/10.1016/j.procs.2020.06.074