Sentiment Analysis of Claude Design Features Using Support Vector Machine on X

Azhar Dwi Nugroho, Budi Prasetiyo

Abstract


The advancement of artificial intelligence technology has introduced various tools that support the design and digital product development process, one of which is the Claude Design feature. The emergence of this technology has generated diverse user responses that can be analyzed through social media platforms. This study aims to analyze user sentiment on the X platform toward the use of the Claude Design feature using the Support Vector Machine (SVM) method. The research data were collected through a web scraping process, resulting in 2,000 posts using the keyword “Claude Design.” The data then underwent preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and lemmatization. Sentiment labeling was performed using the Valence Aware Dictionary and sEntiment Reasoner (VADER) method. After neutral data were removed, a total of 1,438 records remained, consisting of 1,152 positive sentiments and 286 negative sentiments. Subsequently, features were extracted using TF-IDF and classified using the SVM algorithm. The testing results showed that the SVM model achieved an accuracy of 82.29%, precision of 81%, recall of 82%, and F1-score of 82%. The findings indicate that the majority of users express positive sentiment toward Claude Design and demonstrate that the combination of VADER, TF-IDF, and SVM is effective for sentiment analysis on social media data.

Keywords


claude design; sentiment analysis; SVM; VADER; X Platform

References


J. Guo, Y. Yin, L. Sun, and L. Chen, “Empirical Study of Problem-solution Co-evolution in Human-GAI Collaborative Conceptual Design,” DRS2024 Proc., no. 2024, pp. 23–28, 2024, doi: 10.21606/drs.2024.983.

B. Song, Q. Zhu, and J. Luo, “Human-AI Collaboration by Design,” Proc. Des. Soc., vol. 4, pp. 2247–2256, 2024, doi: 10.1017/pds.2024.227.

M. M. Choudhury, B. Eisenbart, and B. Kuys, “Artificial Intelligence ( AI ) in the Design Process – a Review and Analysis on Generative AI Perspectives,” Proc. Des. Soc., vol. 5, pp. 631–640, 2025, doi: 10.1017/pds.2025.10077.

A. Schwarz and W. W. Chin, “Information Technology Acceptance: Construct Development and Empirical Validation,” Int. J. Inf. Manage., vol. 78, no. May, p. 102810, 2024, doi: 10.1016/j.ijinfomgt.2024.102810.

M. K. Chandan and S. Mandal, “A Comprehensive Survey on Sentiment Analysis : Framework , Techniques , and Applications,” Comput. Sci. Rev., vol. 58, no. September 2024, p. 100777, 2025, doi: 10.1016/j.cosrev.2025.100777.

P. Sunarko, A. Bijaksana, P. Negara, and R. Septiriana, “Perbandingan Klasifikasi Algoritma Support vector machine dan Naïve Bayes Menggunakan Labeling VADER dan Lexicon based pada Tweets Bahasa Indonesia dan Bahasa Inggris,” J. Apl. dan Ris. Inform., vol. 03, no. 1, pp. 9–19, 2024, doi: 10.26418/juara.v3i1.86468.

M. R. Ramadhan and K. Budiman, “METHOD Sentiment Analysis of Presidential Candidates in 2024 : A Comparison of the Performance of Support Vector Machine and Random Forest with N-Gram Method,” Recursive J. Informatics, vol. 3, no. 1, pp. 34–42, 2025, doi: 10.15294/rji.v3i1.8385.

M. F. Baihaqi, L. Magdalena, and R. Fahrudin, “Analisis Sentimen Aplikasi Deepseek Menggunakan Metode Naive Bayes dan Support Vector Machine,” RIGGS J. Artif. Intell. Digit. Bus., vol. 4, no. 3, pp. 4051–4062, 2025, doi: https://doi.org/10.31004/riggs.v4i3.2511.

R. Rosandi, A. I. Febrianto, A. A. Gibran, W. Bismi, I. Kurniawati, and R. Fahlapi, “Analisis Sentimen Masyarakat terhadap Penggunaan Gemini AI dengan Metode Machine Learning,” Djtechno J. Teknol. Inf., vol. 6, no. 3, pp. 1105–1118, 2025, doi: 10.46576/djtechno.

S. R. Putri, M. Arifin, and Supriyono, “Analisis Sentimen Publik terhadap Nadiem Makarim sebagai Mendikbudrisktek menggunakan Support Vector Machine (SVM),” Sist. J. Sist. Inf., vol. 14, no. 2, pp. 826–834, 2025, doi: https://doi.org/10.32520/stmsi.v14i2.5067.

R. Sinaga, lham F. Ashari, and W. Yulita, “Analisis Sentimen Kemendikbud menggunakan Vader dan RBF, Polynomial, Linier Kernel SVM Berbasis Binary Particle Swarm Optimization,” Sist. J. Sist. Inf., vol. 13, no. 3, pp. 851–863, 2024, doi: https://doi.org/10.32520/stmsi.v13i3.2186.

J. Rueger, W. Dolfsma, and R. Aalbers, “Mining and Analysing Online Social Networks: Studying the Dynamics of Digital Peer Support,” MethodsX, vol. 10, no. August 2022, p. 102005, 2023, doi: 10.1016/j.mex.2023.102005.

M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P. M. Cuenca-Jiménez, “A Review on Sentiment Analysis from Social Media Platforms,” Expert Syst. Appl., vol. 223, no. August 2022, 2023, doi: 10.1016/j.eswa.2023.119862.

N. Motta, A. Claudia, M. De Souza, C. Marcelo, C. Mario, and D. O. Rodrigues, “Empirical Evaluation of Preprocessing and Balancing Techniques Impact Across Algorithm-Vectorizer Combinations in Sentiment Classification,” An. do Simpósio Bras. Tecnol. da Informação e da Ling. Humana, vol. 16, no. 1, pp. 502–511, 2025, doi: https://doi.org/10.5753/stil.2025.37850.

V. Nurcahyawati and Zuriani Mustaff, “Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation,” J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 109–119, 2023, doi: https://doi.org/10.20473/jisebi.9.1.108-118.

P. Zakiyah, K. Umam, and A. A. Mahfudh, “Public Opinion on The MBG Program : Comparative Evaluation of InSet and VADER Lexicon Labeling Using SVM on Platform X,” J. Appl. Informatics Comput., vol. 9, no. 6, pp. 3937–3944, 2025, doi: https://doi.org/10.30871/jaic.v9i6.9978.

R. A. Pranata and N. A. Verdikha, “Metode Pembobotan TF-IDF untuk Klasifikasi Teks Quick Count Pemilihan Wakil Presiden Indonesia 2024 pada X (Twitter) dengan Metode SVM,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 18, no. 2, pp. 126–138, 2024, doi: https://doi.org/10.47111/jti.v18i2.14934.

M. S. Amrullah, A. G. Putrada, M. N. Fauzan, and N. Alamsyah, “ETLE Sentiment Analysis Performance Increasement with TF-IDF, MDI Feature Selection, and SVM,” Sist. J. Sist. Inf., vol. 13, no. 4, pp. 1308–1318, 2024, doi: https://doi.org/10.32520/stmsi.v13i4.2701.

U. I. Arsyah, M. Pratiwi, and A. Muhammad, “Twitter Sentiment Analysis of Public Space Opinions using SVM and TF-IDF Methods,” Indones. J. Comput. Sci., vol. 13, no. 1, pp. 387–394, 2024, doi: https://doi.org/10.33022/ijcs.v13i1.3594.

I. S. Muharram and M. Faisal, “Tweet Sentiment Classification Towards Mobile Services Using Naive Bayes and Support Vector Machine,” J. KomtekInfo, vol. 12, no. 2, pp. 115–123, 2025, doi: https://doi.org/10.35134/komtekinfo.v12i2.642.




DOI: https://doi.org/10.32520/stmsi.v15i9.6948

Article Metrics

Abstract view : 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.