* Corresponding Author
Hide
Show all
Sentiment Analysis of Movie Opinion in Twitter Using Dynamic Convolutional Neural Network Algorithm
https://doi.org/10.22146/ijccs.19237 Fajar Ratnawati(1*), Edi Winarko(2)
(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Movie has unique characteristics. When someone writes an opinions about a movie, not only the story in the movie itself is written, but also the people involved in the movie are also written. Opinion ordinary movie written in social media primarily twitter.To get a tendency of opinion on the movie, whether opinion is likely positive, negative or neutral, it takes a sentiment analysis. This study aims to classify the sentiment is positive, negative and neutral from opinions Indonesian language movie and look for the accuracy, precission, recall and f-meausre of the method used is Dynamic Convolutional Neural Network. The test results on a system that is built to show that Dynamic Convolutional Neural Network algorithm provides accuracy results better than Naive Bayes method, the value of accuracy of 80,99%, the value of precission 81,00%, recall 81,00%, f-measure 79,00% while the value of the resulting accuracy Naive Bayes amounted to 76,21%, precission 78,00%, recall 76,00%, f-measure 75,00%.
Keywords
Full Text:
PDFReferences
[1] B. Pang, L. Lee, “Opinion Mining and Sentiment Analysis.” Computer Science Department, Cornel University, New York, USA, 2008.
[2] A. Hepburn, Infographic: Twitter Statistics, Facts & Figures, http://www.digitalbuzzblog.com/infographic-twitter-statistics-facts-figures/ diakses tanggal 20 Januari 2017, 2010.
[3] H. Cui, V. Mittal, and M. Datar, “Comparative Experiments on Sentiment Classification for Online Product Reviews.” Department of Computer Science, National University of Singapore. Singapore, 2008.
[4] J. Pustejovsky, and A. Stubbs, “Natural Language Annotation for Machine Learning.” Cambridge University Press, 2012.
[5] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up Sentiment classification using machine learning techniques.” Computer Science Department, Cornell University, New York, USA: Cambridge University Press, 2002.
[6] C.R. Fink, D.S. Chou, J.J. Kopecky, A.J. Llorens, “Coarse and Fine-Grained Sentiment Analisys of Social Media Text.” Johns Hopkins Apl Technical Digest, Volume 30, Number 1, 2011.
[7] R. Feldman, and J. Sanger, “The Text Mining Handbook Addvances Approaches in Analyzing Unstruktured Data.” Cambridge University Press, New York, 2007.
[8] LISA lab, “ Deep Learning Tutorial.” Release 0.1. University of Montreal, 2015.
[9] A. Weibel, T. Hanazawa, G. Hinton, K. Shikano, and K.J. Lang, “Readings in Speech Recognition.” chapter Phoneme Recognition Using Time-delay Neural Networks, pages 393–404. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1990.
[10] R. Collobert, and J. Weston, “A unified architecture for natural language processing: Deep neural nework with multitasking learning.” In International Conference on Machine Learning, ICML, 2008.
[11] N. Kalchbrenner, E. Grefenstette, P. Blunsom, “A Convolutional Neural Network for Modellng Sentence.” Department of Computer Science University of Oxford, 2014.
DOI: https://doi.org/10.22146/ijccs.19237
Article Metrics
Abstract views : 6010
| 
views : 5604
Refbacks
- There are currently no refbacks.
Copyright (c) 2018 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1