BowTie - A deep learning feedforward neural network for sentiment analysis

Published

April 22, 2019

Author(s)

Apostol T. Vassilev

Abstract

How to model and encode the semantics of human-written text and select the type of neural network to process it with are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties are closely related to the loss estimates for the trained model. I present a computationally-efficient and accurate feedforward neural network for sentiment prediction capable of maintaining low losses. When coupled with an effective semantics model of the text it provides highly accurate models with low losses. Losses are maintained low over many epochs of training which allows learning the optimal model for some hyperparameters, e.g. attaining a desired accuracy, for a particular training dataset before transferring it to another and retaining a high transfer accuracy. Experimental results on representative benchmark datasets and comparisons to other methods 1 show the advantages of the new approach.

Citation

arxiv.org

Pub Weblink

https://arxiv.org/

Pub Type

Websites

Download Paper

https://doi.org/10.6028/NIST.CSWP.04222019

Keywords

deep learning, sentiment analysis, machine learning, artificial intelligence

Citation

Vassilev, A. (2019), BowTie - A deep learning feedforward neural network for sentiment analysis, arxiv.org, [online], https://doi.org/10.6028/NIST.CSWP.04222019, https://arxiv.org/ (Accessed October 31, 2024)