[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.17703/IJACT.2017.5.4.57

Long Short Term Memory based Political Polarity Analysis in Cyber Public Sphere

Kang, Hyeon (Department of Computer Engineering, Dongseo University)
Kang, Dae-Ki (Department of Computer Engineering, Dongseo University)

Publication Information

International Journal of Advanced Culture Technology / v.5, no.4, 2017 , pp. 57-62 More about this Journal

Abstract

In this paper, we applied long short term memory(LSTM) for classifying political polarity in cyber public sphere. The data collected from the cyber public sphere is transformed into word corpus data through word embedding. Based on this word corpus data, we train recurrent neural network (RNN) which is connected by LSTM's. Softmax function is applied at the output of the RNN. We conducted our proposed system to obtain experimental results, and we will enhance our proposed system by refining LSTM in our system.

Keywords

Long short term memory (LSTM); Word embedding; Recurrent neural network (RNN); Softmax function;

Citations & Related Records

Reference

1	Y. Bengio, "A Neural Probabilistic Language Model", Journal of Machine Learning Research, Vol. 3, pp. 1137-1155, March 2003.
2	Y. Bengio, P. Simard, and P. Frasconi "Learning Long-Term Dependencies with Gradient Descent is Difficult," IEEE Transactions on Neural Networks, Vol. 51, No. 2, pp. 157-166, March 1994.
3	S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol. 9, No. 8, pp. 1735-1780, 1997. DOI
4	A. Graves, "Supervised Sequence Labelling with Recurrent Neural Networks," Textbook, Studies in Computational Intelligence, Springer, 2012.
5	T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient Estimation of Word Representations in Vector Space," arXiv preprint, arXiv:1301.3781, 2013.