Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification

Charlotte Visser; Flavius Frasincar

doi:10.1007/978-981-96-0579-8_6

Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification

Charlotte Visser, Flavius Frasincar^*

^*Corresponding author for this work

Econometrics

Erasmus University Rotterdam

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

Abstract

Over the last years, enormous amounts of opinions have become available on the Web, which causes the interest in the task of Aspect-Based Sentiment Classification (ABSC) to rise. Hybrid models for ABSC, which combine a knowledge base with a machine learning algorithm, have gained popularity because of their superior performance. LCR-Rot-hop-ont++ is such a hybrid model, which injects knowledge from a domain sentiment ontology into the well-performing attention neural network LCR-Rot-hop++. In this work, we extend the LCR-Rot-hop-ont++ model in two ways. First, we inject additional knowledge from a domain sentiment ontology into the neural network. Second, we apply a novel weighting mechanism to the injected tokens to control the influence of additional knowledge. Using the SemEval 2015 and SemEval 2016 datasets for evaluation, we find that knowledge injection improves accuracy for datasets with a limited number of observations. Furthermore, we find that the proposed weighting mechanism leads to improved predictive performance of the neural network model.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings
Editors	Mahmoud Barhamgi, Hua Wang, Xin Wang
Publisher	Springer Science+Business Media
Pages	74-88
Number of pages	15
ISBN (Print)	9789819605781
DOIs	https://doi.org/10.1007/978-981-96-0579-8_6
Publication status	Published - 2025
Event	25th International Conference on Web Information Systems Engineering, WISE 2024 - Doha, Qatar Duration: 2 Dec 2024 → 5 Dec 2024

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15436 LNCS
ISSN	0302-9743

Conference

Conference	25th International Conference on Web Information Systems Engineering, WISE 2024
Country/Territory	Qatar
City	Doha
Period	2/12/24 → 5/12/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Access to Document

10.1007/978-981-96-0579-8_6

Cite this

Visser, C., & Frasincar, F. (2025). Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification. In M. Barhamgi, H. Wang, & X. Wang (Eds.), Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings (pp. 74-88). Springer Science+Business Media. https://doi.org/10.1007/978-981-96-0579-8_6

Visser, Charlotte ; Frasincar, Flavius. / Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification. Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. editor / Mahmoud Barhamgi ; Hua Wang ; Xin Wang. Springer Science+Business Media, 2025. pp. 74-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 15436 LNCS).

@inproceedings{6d402179e53d4fcb90764253df38c4a9,

title = "Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification",

abstract = "Over the last years, enormous amounts of opinions have become available on the Web, which causes the interest in the task of Aspect-Based Sentiment Classification (ABSC) to rise. Hybrid models for ABSC, which combine a knowledge base with a machine learning algorithm, have gained popularity because of their superior performance. LCR-Rot-hop-ont++ is such a hybrid model, which injects knowledge from a domain sentiment ontology into the well-performing attention neural network LCR-Rot-hop++. In this work, we extend the LCR-Rot-hop-ont++ model in two ways. First, we inject additional knowledge from a domain sentiment ontology into the neural network. Second, we apply a novel weighting mechanism to the injected tokens to control the influence of additional knowledge. Using the SemEval 2015 and SemEval 2016 datasets for evaluation, we find that knowledge injection improves accuracy for datasets with a limited number of observations. Furthermore, we find that the proposed weighting mechanism leads to improved predictive performance of the neural network model.",

author = "Charlotte Visser and Flavius Frasincar",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 25th International Conference on Web Information Systems Engineering, WISE 2024 ; Conference date: 02-12-2024 Through 05-12-2024",

year = "2025",

doi = "10.1007/978-981-96-0579-8\_6",

language = "English",

isbn = "9789819605781",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science+Business Media",

pages = "74--88",

editor = "Mahmoud Barhamgi and Hua Wang and Xin Wang",

booktitle = "Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings",

address = "Germany",

}

Visser, C & Frasincar, F 2025, Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification. in M Barhamgi, H Wang & X Wang (eds), Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. Springer Science+Business Media, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15436 LNCS, pp. 74-88, 25th International Conference on Web Information Systems Engineering, WISE 2024, Doha, Qatar, 2/12/24. https://doi.org/10.1007/978-981-96-0579-8_6

Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification. / Visser, Charlotte; Frasincar, Flavius.
Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. ed. / Mahmoud Barhamgi; Hua Wang; Xin Wang. Springer Science+Business Media, 2025. p. 74-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 15436 LNCS).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

TY - GEN

T1 - Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification

AU - Visser, Charlotte

AU - Frasincar, Flavius

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Over the last years, enormous amounts of opinions have become available on the Web, which causes the interest in the task of Aspect-Based Sentiment Classification (ABSC) to rise. Hybrid models for ABSC, which combine a knowledge base with a machine learning algorithm, have gained popularity because of their superior performance. LCR-Rot-hop-ont++ is such a hybrid model, which injects knowledge from a domain sentiment ontology into the well-performing attention neural network LCR-Rot-hop++. In this work, we extend the LCR-Rot-hop-ont++ model in two ways. First, we inject additional knowledge from a domain sentiment ontology into the neural network. Second, we apply a novel weighting mechanism to the injected tokens to control the influence of additional knowledge. Using the SemEval 2015 and SemEval 2016 datasets for evaluation, we find that knowledge injection improves accuracy for datasets with a limited number of observations. Furthermore, we find that the proposed weighting mechanism leads to improved predictive performance of the neural network model.

AB - Over the last years, enormous amounts of opinions have become available on the Web, which causes the interest in the task of Aspect-Based Sentiment Classification (ABSC) to rise. Hybrid models for ABSC, which combine a knowledge base with a machine learning algorithm, have gained popularity because of their superior performance. LCR-Rot-hop-ont++ is such a hybrid model, which injects knowledge from a domain sentiment ontology into the well-performing attention neural network LCR-Rot-hop++. In this work, we extend the LCR-Rot-hop-ont++ model in two ways. First, we inject additional knowledge from a domain sentiment ontology into the neural network. Second, we apply a novel weighting mechanism to the injected tokens to control the influence of additional knowledge. Using the SemEval 2015 and SemEval 2016 datasets for evaluation, we find that knowledge injection improves accuracy for datasets with a limited number of observations. Furthermore, we find that the proposed weighting mechanism leads to improved predictive performance of the neural network model.

UR - https://www.scopus.com/pages/publications/85211384648

U2 - 10.1007/978-981-96-0579-8_6

DO - 10.1007/978-981-96-0579-8_6

M3 - Conference proceeding

AN - SCOPUS:85211384648

SN - 9789819605781

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 74

EP - 88

BT - Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings

A2 - Barhamgi, Mahmoud

A2 - Wang, Hua

A2 - Wang, Xin

PB - Springer Science+Business Media

T2 - 25th International Conference on Web Information Systems Engineering, WISE 2024

Y2 - 2 December 2024 through 5 December 2024

ER -

Visser C, Frasincar F. Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification. In Barhamgi M, Wang H, Wang X, editors, Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings. Springer Science+Business Media. 2025. p. 74-88. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 15436 LNCS). doi: 10.1007/978-981-96-0579-8_6