Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Fabio Di Troia

Second Advisor

Faranak Abri

Third Advisor

William Andreopoulos


Domain Switch, Sentiment Analysis, Gradient Reversal Layer


Switching domains in sentiment analysis presents the challenge of transferring learned knowledge from one context to another without the need to label data. Traditional methods often struggle when dealing with differences in data distribution a problem known as the domain shift issue. To tackle this using Gradient Reversal Layers (GRL) has emerged as a solution for adapting to different domains in an unsupervised learning setting. This study introduces an enhancement to the standard GRL approach by incorporating a sigmoid function that gradually adjusts how intensely domain adaptation occurs during training. This upgraded GRL technique ensures controlled learning outcomes making it easier for knowledge to transition smoothly between domains. By utilizing sentiment datasets from Amazon Electronics and IMDb reviews the research confirms that this new method outperforms existing approaches. The findings indicate an enhancement in the adaptability and accuracy of sentiment analysis models across diverse domains providing a competitive edge, for industries where flexibility and rapid implementation are crucial.

Available for download on Friday, May 23, 2025