Integrating K-Means Clustering and Levenshtein Distance and K-Nearest Neighbor Algorithms for Enhanced Arabic Sentiment Analysis
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-59711-4_5
Reference22 articles.
1. Al-Shalabi, A.A., Al-Gaphari, G., Salah, A.H., Alqasemi, F.: Investigating the impact of utilizing the K-Nearest neighbor and Levenshtein distance algorithms for Arabic sentiment analysis on mobile applications. Sana'a Univ. J. Appl. Sci. Technol. JAST 1(2) (2023)
2. Al-Hagree, S., Al-Gaphari, G.: Arabic sentiment analysis on mobile applications using Levenshtein distance algorithm and naive Bayes. In: 2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA), pp. 1–6. IEEE (2022)
3. Al-Hagree, S., Al-Gaphari, G.: Arabic sentiment analysis based machine learning for measuring user satisfaction with banking services mobile applications: comparative study. In: 2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA), pp. 1–4. IEEE (2022)
4. Abbes, M., Kechaou, Z., Alimi, A.M.: A novel hybrid model based on CNN and Bi-LSTM for Arabic multi-domain sentiment analysis. In: Barolli, L. (ed.) Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2023, LNDECT, vol. 176, pp. 92–102. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35734-3_10
5. Yahya, M.I., Amrizal, V., Matin, I.M.M., Khairani, D.: Spelling correction using the Levenshtein distance and Nazief and Adriani algorithm for keyword search process Indonesian Quran translation. In: 2022 Seventh International Conference on Informatics and Computing (ICIC), pp. 01–06. IEEE (2022)
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