Citation

Vassilev A. Computer (Long Beach Calif) 2019; 52(10): ePub.

Affiliation

National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, MD 20899, USA.

Copyright

(Copyright © 2019)

DOI

10.1109/mc.2019.2918391

PMID

32127723

PMCID

PMC7053583

Abstract

How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general and are closely related to the viability the trained model. I present a computationally-efficient and accurate feedforward neural network for sentiment prediction capable of maintaining high transfer accuracy when coupled with an effective semantics model of the text. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new approach. Applications to security validation programs are discussed.

Language: en

Keywords

Deep learning; Natural Language Processing; Sentiment analysis