@inproceedings{77a5dac170734913899c5673811ce019,
title = "Analysis of convolution neural network for transfer learning of sentiment analysis in Indonesian tweets",
abstract = "Sentiment analysis is an activity to classify public opinion about entities in textual data into positive or negative. One of the automatic methods for sentiment analysis is convolution neural network (CNN). CNN consists of many layers with many parameters that can be adjusted as needed to form a specific architecture. CNN works well in similar domains; however, it gives less accurate in different domains. Therefore, we consider transfer learning which transfers knowledge from a source domain to different but related target domains. In this paper, we examine parameter sensitivity and accuracy of CNN for transfer learning of sentiment analysis in Indonesian tweets. Our simulation shows that the parameters are very sensitive and incremental learning significantly increases the accuracy of transfer learning of the CNN model.",
keywords = "Convolution neural network, Deep learning, Incremental learning, Sentiment analysis, Transfer learning, Twitter",
author = "Jaya, {Oki Saputra} and Hendri Murfi and Siti Nurrohmah",
year = "2018",
month = jul,
day = "20",
doi = "10.1145/3239283.3239299",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "18--22",
booktitle = "Proceedings of the 2018 International Conference on Data Science and Information Technology, DSIT 2018",
note = "2018 International Conference on Data Science and Information Technology, DSIT 2018 ; Conference date: 20-07-2018 Through 22-07-2018",
}