Design sentiment classification in educational ai platforms: a case study of uknow.ai using svm

Timotius Victory; Muhammad Pramudya Miftah

doi:10.52453/aic.v1iOctober.461

Authors

Timotius Victory ASTRA Polytechnic
Muhammad Pramudya Miftah ASTRA Polytechnic

DOI:

https://doi.org/10.52453/aic.v1iOctober.461

Keywords:

machine learning, sentiment analysis, support vector machine

Abstract

This paper presents a sentiment analysis of user reviews on the Uknow.AI platform, an AI-powered educational tool aimed at assisting students in solving mathematical problems through image recognition. The study utilized a Support Vector Machine (SVM) model to classify user reviews into positive, negative, and neutral sentiments. Data was collected from user reviews on platforms like Google Play, followed by pre-processing steps including tokenization and Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The SVM model was evaluated based on performance metrics such as accuracy, precision, recall, and F1-score. The model achieved an accuracy of 93.42% with a recall of 99.70%, indicating robust performance in sentiment classification. Word cloud visualizations also highlighted dominant positive terms like "bagus" (good) and "membantu" (helpful), emphasizing the overall satisfaction with Uknow.AI's functionality. The study found that 92.6% of the reviews were positive, reflecting the tool’s effectiveness in enhancing student learning. Future research can focus on further improving user experience by addressing any shortcomings.

Author Biographies

Timotius Victory, ASTRA Polytechnic

Software Engineering Technology Department

Muhammad Pramudya Miftah, ASTRA Polytechnic

Software Engineering Technology Department

References

Dea Safryda Putri, Sulistiyowati, N., & A. Voutama. (2023). Analisis Sentimen dan Pemodelan Ulasan Aplikasi AdaKami Menggunakan Algoritma SVM dan KNN. Journal Sensi.

Dinar Ajeng Kristiyanti, Dwi Andini Putri, Indrayuni, E., Acmad Nurhadi, & Akhmad Hairul Umam. (2023). Twitter sentiment analysis using support vector machine and deep learning model in e-learning implementation during the Covid-19 outbreak. AIP Conference Proceedings. https://doi.org/10.1063/5.0128685

Ahuja, R., Chug, A., Kohli, S., Gupta, S., & Ahuja, P. (2019). The Impact of Features Extraction on the Sentiment Analysis. Procedia Computer Science, 152, 341–348. https://doi.org/10.1016/j.procs.2019.05.008

Goniwada, S. R. (2023). Sentiment Analysis. Apress EBooks, 165–184. https://doi.org/10.1007/978-1-4842-9496-3_6

Jin, Y., Cheng, K., Wang, X., & Cai, L. (2023). A Review of Text Sentiment Analysis Methods and Applications. Frontiers in Business, Economics and Management, 10(1), 58–64. https://doi.org/10.54097/fbem.v10i1.10171

Khan, J., Ahmad, N., Khalid, S., Ali, F., & Lee, Y. (2023). Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification. IEEE Access, 11, 28162–28179. https://doi.org/10.1109/access.2023.3259107

Kim, C.-G., Hwang, Y.-J., & Kamyod, C. (2022). A Study of Profanity Effect in Sentiment Analysis on Natural Language Processing Using ANN. Journal of Web Engineering. https://doi.org/10.13052/jwe1540-9589.2139

Liu, H., Chen, X., & Liu, X. (2022). A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis. IEEE Access, 10, 32280–32289. https://doi.org/10.1109/access.2022.3160172

Maitama, J. Z., Idris, N., Abdi, A., Shuib, L., & Fauzi, R. (2020). A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis. IEEE Access, 8, 194166–194191. https://doi.org/10.1109/access.2020.3031217

Maulana Abrari, M. N., & Abdulloh, F. F. (2024). COMPARISON OF MACHINE LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS OF DIGITAL IDENTITY APPLICATION USERS. Jurnal Pilar Nusa Mandiri, 20(2), 146–154. https://doi.org/10.33480/pilar.v20i2.5736

Mukarramah, R., Atmajaya, D., & Ilmawan, L. B. (2021). Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter. ILKOM Jurnal Ilmiah, 13(2), 168–174. https://doi.org/10.33096/ilkom.v13i2.851.168-174

Study. (2019, January 29). Uknow. AppAdvice. https://appadvice.com/app/uknow-ai-homework-math-solver/1447618956

Uknow.AI, Instant Homework Helper. (2024). Checkmath.net. https://www.checkmath.net/

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(55). https://doi.org/10.1007/s10462-022-10144-1

Zainuddin, N., & Selamat, A. (2014). Sentiment analysis using Support Vector Machine. 2014 International Conference on Computer, Communications, and Control Technology (I4CT). https://doi.org/10.1109/i4ct.2014.6914200