Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers

Kunal Geed; Flavius Frasincar; Maria Mihaela Truşcǎ

doi:10.1007/978-3-031-09917-5_18

Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers

Kunal Geed, Flavius Frasincar, Maria Mihaela Truşcǎ^*

^*Corresponding author for this work

Econometrics

Research output: Chapter/Conference proceeding › Chapter › Academic

4 Citations (Scopus)

Abstract

LCR-Rot-hop++ is a state-of-art model for Aspect-Based Sentiment Classification. However, it is also a black-box model where the information encoded in each layer is not understood by the user. This study uses diagnostic classifiers, single layer neural networks, to evaluate the information encoded in each layer of the LCR-Rot-hop++ model. This is done by using various hypotheses designed to test for information deemed useful for sentiment analysis. We conclude that the model did not focus on identifying the aspect mentions associated with a word and the structure of the sentence. However, the model excelled in encoding information to identify which words are related to the target. Lastly, the model was able to encode to some extent information about the word sentiment and sentiments of the words related to the target.

Original language	English
Title of host publication	Web Engineering - 22nd International Conference, ICWE 2022, Proceedings
Editors	Tommaso Di Noia, In-Young Ko, Markus Schedl, Carmelo Ardito
Publisher	Springer Science+Business Media
Pages	268-282
Number of pages	15
Volume	13362
ISBN (Print)	9783031099168
DOIs	https://doi.org/10.1007/978-3-031-09917-5_18
Publication status	Published - 2022
Event	22nd International Conference on Web Engineering, ICWE 2022 - Bari, Italy Duration: 5 Jul 2022 → 8 Jul 2022

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13362 LNCS
ISSN	0302-9743

Conference

Conference	22nd International Conference on Web Engineering, ICWE 2022
Country/Territory	Italy
City	Bari
Period	5/07/22 → 8/07/22

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-031-09917-5_18

Cite this

Geed, K., Frasincar, F., & Truşcǎ, M. M. (2022). Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers. In T. Di Noia, I.-Y. Ko, M. Schedl, & C. Ardito (Eds.), Web Engineering - 22nd International Conference, ICWE 2022, Proceedings (Vol. 13362, pp. 268-282). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-09917-5_18

Geed, Kunal ; Frasincar, Flavius ; Truşcǎ, Maria Mihaela. / Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers. Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. editor / Tommaso Di Noia ; In-Young Ko ; Markus Schedl ; Carmelo Ardito. Vol. 13362 Springer Science+Business Media, 2022. pp. 268-282 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13362 LNCS).

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abstract = "LCR-Rot-hop++ is a state-of-art model for Aspect-Based Sentiment Classification. However, it is also a black-box model where the information encoded in each layer is not understood by the user. This study uses diagnostic classifiers, single layer neural networks, to evaluate the information encoded in each layer of the LCR-Rot-hop++ model. This is done by using various hypotheses designed to test for information deemed useful for sentiment analysis. We conclude that the model did not focus on identifying the aspect mentions associated with a word and the structure of the sentence. However, the model excelled in encoding information to identify which words are related to the target. Lastly, the model was able to encode to some extent information about the word sentiment and sentiments of the words related to the target.",

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Geed, K, Frasincar, F & Truşcǎ, MM 2022, Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers. in T Di Noia, I-Y Ko, M Schedl & C Ardito (eds), Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. vol. 13362, Springer Science+Business Media, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13362 LNCS, pp. 268-282, 22nd International Conference on Web Engineering, ICWE 2022, Bari, Italy, 5/07/22. https://doi.org/10.1007/978-3-031-09917-5_18

Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers. / Geed, Kunal; Frasincar, Flavius; Truşcǎ, Maria Mihaela.
Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. ed. / Tommaso Di Noia; In-Young Ko; Markus Schedl; Carmelo Ardito. Vol. 13362 Springer Science+Business Media, 2022. p. 268-282 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13362 LNCS).

Research output: Chapter/Conference proceeding › Chapter › Academic

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Geed K, Frasincar F, Truşcǎ MM. Explaining a Deep Neural Model with Hierarchical Attention for Aspect-Based Sentiment Classification Using Diagnostic Classifiers. In Di Noia T, Ko IY, Schedl M, Ardito C, editors, Web Engineering - 22nd International Conference, ICWE 2022, Proceedings. Vol. 13362. Springer Science+Business Media. 2022. p. 268-282. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 13362 LNCS). doi: 10.1007/978-3-031-09917-5_18