<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Sentiment analysis is a useful tool in several social and business contexts. Aspect sentiment classification is a subtask in sentiment analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different deep learning models that have been proposed to solve aspect sentiment classification focus on a specific domain such as restaurant, hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The continual learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoiding low, aspect classification performance in continual learning is challenging. As a consequence, potential neural network weight shifts in the learning process in different domains or datasets.</p>
<p>In this paper, a novel aspect sentiment classification approach is proposed. Our approach combines a transformer deep learning technique with a continual learning algorithm in different domains. The input layer used is the pretrained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 % F1-macro. Our results improve other approaches from the state-of-the-art.</p>
</abstract>

Sentiment analysis is a useful tool in several social and business contexts. Aspect sentiment classification is a subtask in sentiment analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different deep learning models that have been proposed to solve aspect sentiment classification focus on a specific domain such as restaurant, hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The continual learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoiding low, aspect classification performance in continual learning is challenging. As a consequence, potential neural network weight shifts in the learning process in different domains or datasets.
In this paper, a novel aspect sentiment classification approach is proposed. Our approach combines a transformer deep learning technique with a continual learning algorithm in different domains. The input layer used is the pretrained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 % F1-macro. Our results improve other approaches from the state-of-the-art.

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Universidad de Oriente Ave. Patricio Lumumba s/n, Santiago de Cuba

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis"}

Sentiment analysis is a useful tool in several social and business contexts. Aspect sentiment classification is a subtask in sentiment analysis that gives...

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Publié en ligne: 26 déc. 2023

Pages: 3 - 12

Reçu: 10 janv. 2023

Accepté: 18 févr. 2023

DOI: https://doi.org/10.14313/jamris/1-2023/1

Mots clés
Continual Learning, Deep Learning, Catastrophic Forgetting, Sentiment Analysis

© 2023 Dionis López et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Publié en ligne: 26 déc. 2023

Pages: 3 - 12

Reçu: 10 janv. 2023

Accepté: 18 févr. 2023

DOI: https://doi.org/10.14313/jamris/1-2023/1

Mots clésContinual Learning, Deep Learning, Catastrophic Forgetting, Sentiment Analysis

© 2023 Dionis López et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Continual Learning, Deep Learning, Catastrophic Forgetting, Sentiment Analysis