Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film
Abstract
Full Text:
PDFReferences
Dergiades, T. (2012). Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy. Economics Letters, 116(3), 404–407. doi:10.1016/j.econlet.2012.04.018
Forman, G. (2000). An Extensive Empirical Study of Feature Selection Metrics for Text Classification. Journal of Machine Learning Research, 3, 1289–1305. doi:10.1162/153244303322753670
Kang, H., Yoo, S. J., & Han, D. (2012). Senti lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications, 39(5), 6000–6010. doi:10.1016/j.eswa.2011.11.107
Koh, N. S., Hu, N., & Clemons, E. K. (2010). Do online reviews reflect a product’s true perceived quality? An investigation of online movie reviews across cultures. Electronic Commerce Research and Applications, 9(5), 374–385. doi:10.1016/j.elerap.2010.04.001
Koncz, P., & Paralic, J. (2011). An approach to feature selection for sentiment analysis. In 2011 15th IEEE International Conference on Intelligent Engineering Systems (pp. 357–362). IEEE. doi:10.1109/INES.2011.5954773
Kontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications, 40(10), 4065–4074. doi:10.1016/j.eswa.2013.01.001
Langgeni, D. P., Baizal, Z. K. A., & W, Y. F. A. (2010). Clustering Artikel Berita Berbahasa Indonesia, 2010(semnasIF), 1–10.
Liu, C.-L., Hsaio, W.-H., Lee, C.-H., Lu, G.-C., & Jou, E. (2012). Movie Rating and Review Summarization in Mobile Environment. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(3), 397–407. doi:10.1109/TSMCC.2011.2136334
Liu, Y., Huang, X., An, A., & Yu, X. (2007). ARSA: A Sentiment-Aware Model for Predicting Sales Performance Using Blogs. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’07 (p. 607). New York, New York, USA: ACM Press. doi:10.1145/1277741.1277845
Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011). An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, 8(2), 191–200. doi:10.1016/S1672-6529(11)60020-6
Manning, C. D., Raghavan, P., & Schutze, H. (n.d.). Introduction to Information Retrieval.
Moraes, R., Valiati, J. F., & Gavião Neto, W. P. (2013). Document Level Sentiment Classification: an Empirical Comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621–633. doi:10.1016/j.eswa.2012.07.059
Nugroho, A. S., Witarto, A. B., & Handoko, D. (2003). Support Vector Machine Teori dan Aplikasinya dalam Bioinformatika. IlmuKomputer.Com.
Pang, B., & Lee, L. (2002). A Sentimental Education : Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Association for Computational Linguistics.
Pang, B., Lee, L., Rd, H., & Jose, S. (2002). Thumbs up ? Sentiment Classification using Machine Learning Techniques. Association for Computational Linguistics, 10(July), 79–86.
Park, S., Ko, M., Kim, J., Liu, Y., & Song, J. (2011). The Politics of Comments : Predicting Political Orientation of News Stories with Commenters ’ Sentiment Patterns.
Tan, S., & Wang, Y. (2011). Weighted SCL model for adaptation of sentiment classification. Expert Systems with Applications, 38(8), 10524–10531. doi:10.1016/j.eswa.2011.02.106
Tan, S., & Zhang, J. (2008). An empirical study of sentiment analysis for chinese documents. Expert Systems with Applications, 34(4), 2622–2629. doi:10.1016/j.eswa.2007.05.028
Tsou, B. K., & Ma, M. (2011). Aspect Based Opinion Polling from Customer Reviews. IEEE Transactions on Affective Computing, 2(1), 37–49. doi:10.1109/T-AFFC.2011.2
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council, 10(5), 988–99. doi:10.1109/72.788640
Vercellis, C. (2009). Business Intelligence: Data Mining and Optomization for Decision Making. John Wiley and Sons.
Wang, S., Li, D., Song, X., Wei, Y., & Li, H. (2011). A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Systems with Applications, 38(7), 8696–8702. doi:10.1016/j.eswa.2011.01.077
Wang, S., Li, D., Zhao, L., & Zhang, J. (2013). Sample cutting method for imbalanced text sentiment classification based on BRC. Knowledge-Based Systems, 37, 451–461. doi:10.1016/j.knosys.2012.09.003
Xu, T., Peng, Q., & Cheng, Y. (2012). Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowledge-Based Systems, 35, 279–289. doi:10.1016/j.knosys.2012.04.011
Yang, Y., & Pedersen, J. O. (1997). A Comparative Study on Feature Selection in Text Categorization. Proceedings of the Fourteenth International Conference on Machine Learning, 20(15), 412–420.
Zhang, W., & Gao, F. (2011). An Improvement to Naive Bayes for Text Classification. Advanced in Control Engineeringand Information Science, 15, 2160–2164. doi:10.1016/j.proeng.2011.08.404
Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6), 7674–7682. doi:10.1016/j.eswa.2010.12.147
Zhu, J., Xu, C., & Wang, H. (2010). Sentiment classification using the theory of ANNs. The Journal of China Universities of Posts and Telecommunications, 17(July), 58–62. doi:10.1016/S1005-8885(09)60606-3
Refbacks
- There are currently no refbacks.
Journal of Intelligent Systems(JIS, ISSN 2356-3982) Copyright © 2020IlmuKomputer.Com. All rights reserved. |