Abstract: Humans have the power to feel different types of emotions because human life is filled with many emotions. Human’s emotion can be reflected through reading or writing a text. In recent years, studies on emotion detection through text has been developed. Most of the study is using a machine learning technique. In this paper, we classified 7 emotions such as anger, fear, joy, love, sadness, surprise, and thankfulness using deep learning technique that is Long Short-Term Memory (LSTM) and Nested Long Short-Term Memory (Nested LSTM). We have compared our results with Support Vector Machine (SVM). We have trained each model with 980,549 training data and tested with 144,160 testing data. Our experiments showed that Nested LSTM and LSTM give better performance than SVM to detect emotions in text. Nested LSTM gets the best accuracy of 99.167%, while LSTM gets the best performance in term of average precision at 99.22%, average recall at 98.86%, and f1-score at 99.04%.

Daniel Haryadi and Gede Putra Kusuma, “Emotion Detection in Text using Nested Long Short-Term Memory” International Journal of Advanced Computer Science and Applications(IJACSA), 10(6), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100645

@article{Haryadi2019,
title = {Emotion Detection in Text using Nested Long Short-Term Memory},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100645},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100645},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Daniel Haryadi and Gede Putra Kusuma}
}