2 research outputs found
Effectiveness of Self Normalizing Neural Networks for Text Classification
Self Normalizing Neural Networks(SNN) proposed on Feed Forward Neural
Networks(FNN) outperform regular FNN architectures in various machine learning
tasks. Particularly in the domain of Computer Vision, the activation function
Scaled Exponential Linear Units (SELU) proposed for SNNs, perform better than
other non linear activations such as ReLU. The goal of SNN is to produce a
normalized output for a normalized input. Established neural network
architectures like feed forward networks and Convolutional Neural Networks(CNN)
lack the intrinsic nature of normalizing outputs. Hence, requiring additional
layers such as Batch Normalization. Despite the success of SNNs, their
characteristic features on other network architectures like CNN haven't been
explored, especially in the domain of Natural Language Processing. In this
paper we aim to show the effectiveness of proposed, Self Normalizing
Convolutional Neural Networks(SCNN) on text classification. We analyze their
performance with the standard CNN architecture used on several text
classification datasets. Our experiments demonstrate that SCNN achieves
comparable results to standard CNN model with significantly fewer parameters.
Furthermore it also outperforms CNN with equal number of parameters.Comment: Accepted Long Paper at 20th International Conference on Computational
Linguistics and Intelligent Text Processing, April 2019, La Rochelle, Franc
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
Internet forums and public social media, such as online healthcare forums,
provide a convenient channel for users (people/patients) concerned about health
issues to discuss and share information with each other. In late December 2019,
an outbreak of a novel coronavirus (infection from which results in the disease
named COVID-19) was reported, and, due to the rapid spread of the virus in
other parts of the world, the World Health Organization declared a state of
emergency. In this paper, we used automated extraction of COVID-19 related
discussions from social media and a natural language process (NLP) method based
on topic modeling to uncover various issues related to COVID-19 from public
opinions. Moreover, we also investigate how to use LSTM recurrent neural
network for sentiment classification of COVID-19 comments. Our findings shed
light on the importance of using public opinions and suitable computational
techniques to understand issues surrounding COVID-19 and to guide related
decision-making