Exploiting Entity Information for Stream Classification over a Stream of Reviews

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

  • Christian Beyer
  • Pawel Matuszyk
  • Vishnu Unnikrishnan
  • Eirini Ntoutsi
  • Uli Niemann
  • Myra Spiliopoulou

Organisationseinheiten

Externe Organisationen

  • Otto-von-Guericke-Universität Magdeburg
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksSAC '19
UntertitelProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
Seiten564-573
Seitenumfang10
PublikationsstatusVeröffentlicht - 8 Apr. 2019
Veranstaltung34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Zypern
Dauer: 8 Apr. 201912 Apr. 2019

Abstract

Opinion stream classification algorithms adapt the model to the arriving review texts and, depending on the forgetting scheme, reduce the contribution old reviews have upon the model. Reviews are assumed independent, and information on the entity to which a review refers, i.e. to the opinion target, is thereby ignored. This implies that the prediction of a review's label is based more on reviews referring to other, more popular or simply more recently inspected entities, while reviews referring to the same entity might be ignored as too old. In this study, we enforce that the reviews to each entity are taken into account for learning, adaption and forgetting. We split the original stream to substreams, each substream comprised by the reviews referring to the same entity (opinion target). This allows us to deal with differences in the speed of each substream and to exploit the impact of the entity itself on the labels of the reviews referring to it. For this constellation of substreams we propose a pair of two voting classifiers, one being the global, “entity-ignorant” classifier trained on the whole stream of reviews, the other one consisting of one “entity-centric” classifier per entity. We show that the entity-ignorant classifier contributes most for entities with very few reviews, i.e. during the cold-start, while the entity-centric classifiers contribute most after acquiring enough information on the corresponding entities. We study our approach on a stream of product reviews, show that our ensemble improves the performance of its members, and we discuss the conditions under which one member contributes more than the other.

ASJC Scopus Sachgebiete

Zitieren

Exploiting Entity Information for Stream Classification over a Stream of Reviews. / Beyer, Christian; Matuszyk, Pawel; Unnikrishnan, Vishnu et al.
SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019. S. 564-573.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Beyer, C, Matuszyk, P, Unnikrishnan, V, Ntoutsi, E, Niemann, U & Spiliopoulou, M 2019, Exploiting Entity Information for Stream Classification over a Stream of Reviews. in SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. S. 564-573, 34th Annual ACM Symposium on Applied Computing, SAC 2019, Limassol, Zypern, 8 Apr. 2019. https://doi.org/10.1145/3297280.3297333
Beyer, C., Matuszyk, P., Unnikrishnan, V., Ntoutsi, E., Niemann, U., & Spiliopoulou, M. (2019). Exploiting Entity Information for Stream Classification over a Stream of Reviews. In SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (S. 564-573) https://doi.org/10.1145/3297280.3297333
Beyer C, Matuszyk P, Unnikrishnan V, Ntoutsi E, Niemann U, Spiliopoulou M. Exploiting Entity Information for Stream Classification over a Stream of Reviews. in SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019. S. 564-573 doi: 10.1145/3297280.3297333
Beyer, Christian ; Matuszyk, Pawel ; Unnikrishnan, Vishnu et al. / Exploiting Entity Information for Stream Classification over a Stream of Reviews. SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019. S. 564-573
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title = "Exploiting Entity Information for Stream Classification over a Stream of Reviews",
abstract = "Opinion stream classification algorithms adapt the model to the arriving review texts and, depending on the forgetting scheme, reduce the contribution old reviews have upon the model. Reviews are assumed independent, and information on the entity to which a review refers, i.e. to the opinion target, is thereby ignored. This implies that the prediction of a review's label is based more on reviews referring to other, more popular or simply more recently inspected entities, while reviews referring to the same entity might be ignored as too old. In this study, we enforce that the reviews to each entity are taken into account for learning, adaption and forgetting. We split the original stream to substreams, each substream comprised by the reviews referring to the same entity (opinion target). This allows us to deal with differences in the speed of each substream and to exploit the impact of the entity itself on the labels of the reviews referring to it. For this constellation of substreams we propose a pair of two voting classifiers, one being the global, “entity-ignorant” classifier trained on the whole stream of reviews, the other one consisting of one “entity-centric” classifier per entity. We show that the entity-ignorant classifier contributes most for entities with very few reviews, i.e. during the cold-start, while the entity-centric classifiers contribute most after acquiring enough information on the corresponding entities. We study our approach on a stream of product reviews, show that our ensemble improves the performance of its members, and we discuss the conditions under which one member contributes more than the other.",
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note = "Funding Information: This work is partially funded by the German Research Foundation, project OSCAR {"}Opinion Stream Classification with Ensembles and Active Learners{"}. Additionally, the first author is also partially funded by a PhD grant from the federal state of Saxony-Anhalt.; 34th Annual ACM Symposium on Applied Computing, SAC 2019 ; Conference date: 08-04-2019 Through 12-04-2019",
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AU - Beyer, Christian

AU - Matuszyk, Pawel

AU - Unnikrishnan, Vishnu

AU - Ntoutsi, Eirini

AU - Niemann, Uli

AU - Spiliopoulou, Myra

N1 - Funding Information: This work is partially funded by the German Research Foundation, project OSCAR "Opinion Stream Classification with Ensembles and Active Learners". Additionally, the first author is also partially funded by a PhD grant from the federal state of Saxony-Anhalt.

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Y1 - 2019/4/8

N2 - Opinion stream classification algorithms adapt the model to the arriving review texts and, depending on the forgetting scheme, reduce the contribution old reviews have upon the model. Reviews are assumed independent, and information on the entity to which a review refers, i.e. to the opinion target, is thereby ignored. This implies that the prediction of a review's label is based more on reviews referring to other, more popular or simply more recently inspected entities, while reviews referring to the same entity might be ignored as too old. In this study, we enforce that the reviews to each entity are taken into account for learning, adaption and forgetting. We split the original stream to substreams, each substream comprised by the reviews referring to the same entity (opinion target). This allows us to deal with differences in the speed of each substream and to exploit the impact of the entity itself on the labels of the reviews referring to it. For this constellation of substreams we propose a pair of two voting classifiers, one being the global, “entity-ignorant” classifier trained on the whole stream of reviews, the other one consisting of one “entity-centric” classifier per entity. We show that the entity-ignorant classifier contributes most for entities with very few reviews, i.e. during the cold-start, while the entity-centric classifiers contribute most after acquiring enough information on the corresponding entities. We study our approach on a stream of product reviews, show that our ensemble improves the performance of its members, and we discuss the conditions under which one member contributes more than the other.

AB - Opinion stream classification algorithms adapt the model to the arriving review texts and, depending on the forgetting scheme, reduce the contribution old reviews have upon the model. Reviews are assumed independent, and information on the entity to which a review refers, i.e. to the opinion target, is thereby ignored. This implies that the prediction of a review's label is based more on reviews referring to other, more popular or simply more recently inspected entities, while reviews referring to the same entity might be ignored as too old. In this study, we enforce that the reviews to each entity are taken into account for learning, adaption and forgetting. We split the original stream to substreams, each substream comprised by the reviews referring to the same entity (opinion target). This allows us to deal with differences in the speed of each substream and to exploit the impact of the entity itself on the labels of the reviews referring to it. For this constellation of substreams we propose a pair of two voting classifiers, one being the global, “entity-ignorant” classifier trained on the whole stream of reviews, the other one consisting of one “entity-centric” classifier per entity. We show that the entity-ignorant classifier contributes most for entities with very few reviews, i.e. during the cold-start, while the entity-centric classifiers contribute most after acquiring enough information on the corresponding entities. We study our approach on a stream of product reviews, show that our ensemble improves the performance of its members, and we discuss the conditions under which one member contributes more than the other.

KW - Document Prediction

KW - Entity-Centric Learning

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