TY - GEN
T1 - Performance comparison of text-based sentiment analysis using recurrent neural network and convolutional neural network
AU - Purnamasari, Prima Dewi
AU - Taqyuddin, null
AU - Ratna, Anak Agung Putri
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/24
Y1 - 2017/11/24
N2 - One biggest challenge in sentiment analysis is that it should include Natural Language Processing (NLP), to make the machine understand the human language. With the current development of Artificial Neural Network (ANN), with its implementation, computer can learn to understand human language by such learning mechanism There are many types of ANN and for this research Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were used and compared on their performance. The text data for the sentiment analysis was taken from Stanford publication and transformation from text to vectors were conducted using word2vec. The result shows that RNN is better than CNN. Even the difference of accuracy is not significant with 88.35% ±0.07 for RNN and 87.11% ±0.50 for CNN, the training time for RNN only need 8.256 seconds while CNN need 544.366 seconds.
AB - One biggest challenge in sentiment analysis is that it should include Natural Language Processing (NLP), to make the machine understand the human language. With the current development of Artificial Neural Network (ANN), with its implementation, computer can learn to understand human language by such learning mechanism There are many types of ANN and for this research Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were used and compared on their performance. The text data for the sentiment analysis was taken from Stanford publication and transformation from text to vectors were conducted using word2vec. The result shows that RNN is better than CNN. Even the difference of accuracy is not significant with 88.35% ±0.07 for RNN and 87.11% ±0.50 for CNN, the training time for RNN only need 8.256 seconds while CNN need 544.366 seconds.
KW - Convolutional Neural Network
KW - Natural Language Processing
KW - Recurrent Neural Network
KW - Sentiment analysis
KW - Word2vec
UR - http://www.scopus.com/inward/record.url?scp=85042077644&partnerID=8YFLogxK
U2 - 10.1145/3162957.3163012
DO - 10.1145/3162957.3163012
M3 - Conference contribution
AN - SCOPUS:85042077644
T3 - ACM International Conference Proceeding Series
SP - 19
EP - 23
BT - Proceedings of the 3rd International Conference on Communication and Information Processing, ICCIP 2017
PB - Association for Computing Machinery
T2 - 3rd International Conference on Communication and Information Processing, ICCIP 2017
Y2 - 24 November 2017 through 26 November 2017
ER -