1 |
Chen, M. H., Chen, W. F., and Ku, L. W., "Application of Sentiment Analysis to Language Learning," in IEEE Access, Vol. 6, pp. 24433-24442, 2018.
DOI
|
2 |
Day, M. Y., and Lin, Y. D., "Deep Learning for Sentiment Analysis on Google Play Consumer Review," 2017 IEEE International Conference on Information Reuse and Integration (IRI), San Diego, CA, pp. 382-388, 2017.
|
3 |
Hassan, A., and Mahmood, A., "Deep Learning Approach for Sentiment Analysis of Short Texts," 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), Nagoya, pp. 705-710, 2017.
|
4 |
Jianqiang, Z., Xiaolin, G., and Xuejun, Z., "Deep Convolution Neural Networks for Twitter Sentiment Analysis," in IEEE Access, Vol. 6, pp. 23253-23260, 2018.
DOI
|
5 |
Deng, L. and Dong, Y., "Deep Learning: Methods and Applications," NOW Publishers, United State of America, 2014.
|
6 |
Aaron, Basic Korean Sentence Structure, 2014. [Online]. Available at http://keytokorean.com/classes/beginner/basic-korean-sentence-structure/ [Accessed 20 May 2017].
|
7 |
Vidhya Content Team, Quick Guide: Steps to Perform Text Data Cleaning in Python, 2015. [Online]. Available at https://www.analyticsvidhya.com/blog/2015/06/quick-guide-text-data-cleaninGoodfellow-et-al-2016 [Accessed 20 May 2017].
|
8 |
Tomar, S.S., Text mining in R: A Tutorial, 2017 [Online]. Available at https://www.springboard.com/blog/text-mining-in-r/ [Accessed 20 May 2017].
|
9 |
Yuhang, Z., Yue, W., and Wei, Y., "Research on Data Cleaning in Text Clustering," 2010 International Forum on Information Technology and Applications, Kunming, pp. 305-307, 2010.
|
10 |
Github, Twitter-Korean-text, 2014. [Online]. Available at https://github.com/twitter/twitter-korean-text [Accessed 20 May 2017].
|
11 |
Quora, What Are All The Speech Levels of Korean and How Are They Used?, 2012. [Online]. Available at https://www.quora.com/What-are-all-the-speech-levels-of-Korean-and-how-are-they-used [Accessed 20 May 2017].
|
12 |
Miachel, R0., 3 Steps of Text Mining, 2012. [Online]. Available at http://www2.cs.man.ac.uk/-raym8/comp38212/main/node203.html [Accessed 20 May 2017].
|
13 |
Yun, B. H., “Natural Language Processing-based Information Extraction for Newspapers,” Journal of Korean Institute of Information Technology, Vol. 6, No. 4, pp. 188-195, 2008.
|
14 |
Duncan, B., and Zhang, Y., "Neural Networks For Sentiment Analysis on Twitter," 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Beijing, pp. 275-278, 2015.
|
15 |
Lavanya, K. and Deisy, C., "Twitter Sentiment Analysis using Multi-Class SVM," 2017 International Conference on Intelligent Computing and Control (I2C2), Coimbatore, pp. 1-6, 2017.
|
16 |
Joshi, R. and Tekchandani, R., "Comparative Analysis of Twitter Data using Supervised Classifiers," 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, pp. 1-6, 2016.
|
17 |
Ramadhani, R. A., Indriani, F., and Nugrahadi, D. T., "Comparison of Naive Bayes Smoothing Methods for Twitter Sentiment Analysis," 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang, pp. 287-292, 2016.
|
18 |
Lee, J. H. and Lee, H. K., “A Study on Unstructured Text Mining Algorithm through R Programming based on Data Dictionary,” Journal of the Korea Society Industrial Information System, Vol. 20, No. 2, pp. 113-12, 2015.
DOI
|
19 |
GoodfellowI, Y., Bengio, and Courville, B., 2016. Deep Learning. MIT Press [Online]. Available at http://www.deeplearningbook.org [Accessed 20 May 2017].
|
20 |
Ruder, S., An Overview of Gradient Descent Optimization Algorithms, 2016. [Online]. Available at http://sebastianruder.com/optimizing-gradient-descent/ [Accessed 20 May 2017].
|
21 |
Scikit Learn Team. 2016. Stochastic Gradient Descent [Online]. Available at http://scikit-learn.org/stable/modules/sgd.html [Accessed 20 May 2017].
|
22 |
Karim, M., Deep Learning via Multilayer Perceptron Classifier - Dzone Big Data, 2018. [Online]. dzone.com. Available at https://dzone.com/articles/deep-learning-viamultilayer-perceptron-classifier [Accessed 13 June 2018].
|
23 |
Blei, D. M., Ng, A. Y., and Jordan, M. I., “Latent Dirichlet Allocation,” Journal of Machine Learning Research, Vol. 3, No. 5, pp. 993-1022, 2003.
|
24 |
Wang, D., Thint, M., and Al-Rubaie. A., "Semi-Supervised Latent Dirichlet Allocation and Its Application for Document Classification," 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 306-310, 2012.
|
25 |
Yee, C. S. and Ahmad, A. M., "Malay Language Text-Independent Speaker Verification using Nn-Mlp Classifier with Mfcc," 2008 International Conference on Electronic Design, Penang, pp. 1-5, 2008.
|
26 |
Barde, B. V. and Bainwad, A. M., "An Overview of Topic Modeling Methods and Tools," 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, pp. 745-750, 2017.
|
27 |
Ahn, H., “A Study on Compression of Connections in Deep Artificial Neural Networks,” Journal of the Korea Industrial Information Systems Research, Vol. 22, No. 5, pp. 17-24, 2017.
DOI
|
28 |
Nalini, S. Sandhya, K. Ganesh Kumar, P., "Enhancing Gender Classification in Social Networks," 2014 The International Industrial Information Systems Conference, pp. 251-256, 2014.
|
29 |
Nielsen, M., Using Neural Nets to Recognize Handwritten Digits, 2017. [Online]. Available at http://neuralnetworksanddeeplearning.com/chap1.html [Accessed 20 May 2017].
|