@inproceedings{d1dae09754af4a5693b1b424f4e75fed,
title = "A Self-Weighting Module to Improve Sentiment Analysis",
abstract = "This article introduces a self-weighting module for filtering meaningless words and normalizing them before RNN encoding, with the purpose of alleviating the long-term dependencies problem. We make use of the concept of weights in our design to analyze the transition of hidden states and indicate the complete architecture for processing the weighted feature and embedded word within the proposed module. In particular, we investigate the conditions that can enhance convergence and show that the proposed classifiers are able to improve the accuracy in the experimental cases significantly, not only giving better performance but also producing faster convergence. Moreover, the proposed module is general and can be applied to all RNN related network models.",
keywords = "CARU, NLP, Self-Weighting, Sentiment Analysis, Sign Function",
author = "Chan, {Ka Hou} and Im, {Sio Kei} and Yunfeng Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",
year = "2021",
month = jul,
day = "18",
doi = "10.1109/IJCNN52387.2021.9533887",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings",
address = "United States",
}