BowTie - a deep learning feedforward neural network for sentiment analysis

Published

January 3, 2020

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 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. Experimental results on representative benchmark datasets and comparisons to other methods.

Proceedings Title

The 5th International Conference on machine Learning, Optimization and Data science - LOD 2019

Volume

11943

Conference Dates

September 10-13, 2019

Conference Location

Siena

Conference Title

LOD 2019

Pub Type

Conferences

Download Paper

https://doi.org/10.1007/978-3-030-37599-7_30

Local Download

Keywords

Deep learning, Sentiment analysis, Natural Language Processing

Citation

Vassilev, A. (2020), BowTie - a deep learning feedforward neural network for sentiment analysis, The 5th International Conference on machine Learning, Optimization and Data science - LOD 2019, Siena, -1, [online], https://doi.org/10.1007/978-3-030-37599-7_30 (Accessed October 1, 2025)