A micro study on the evolution of arguments in amazon.Com’s reviews

Simone Gabbriellini; Francesco Santini

doi:10.1007/978-3-319-25524-8_18

A micro study on the evolution of arguments in amazon.Com’s reviews

Simone Gabbriellini
, Francesco Santini^*

^*Autore corrispondente per questo lavoro

University of Perugia

Risultato della ricerca: Contributo in libro › Contributo a conferenza

Abstract

In this work we present an exploratory study on arguments in Amazon.com reviews. We manually extract positive (in favour of purchase) and negative (against it) arguments from each review concerning a selected product. Moreover, we link arguments to the rating score and length of reviews. For instance, we show that negative arguments are quite sparse during the first steps of such social review-process, while positive arguments are more equally distributed. In addition, we connect arguments through attacks and we compute Dung’s extensions to check whether they capture such evolution through time. We also use Preference-based Argumentation to exploit the number of appearances of each argument in reviews.

Lingua originale	Inglese
Titolo della pubblicazione ospite	PRIMA 2015: Principles and Practice of Multi-Agent Systems
Pagine	284-300
Numero di pagine	17
Volume	9387
DOI	https://doi.org/10.1007/978-3-319-25524-8_18
Stato di pubblicazione	Pubblicato - 2015
Evento	18th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA) - Bertinoro (FC), Italy Durata: 26 giu 2015 → 30 giu 2015

Serie di pubblicazioni

Nome	LECTURE NOTES IN COMPUTER SCIENCE

Convegno

Convegno	18th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)
Città	Bertinoro (FC), Italy
Periodo	26/6/15 → 30/6/15

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Keywords

consumer behavior
computational argumentation
agent-based models

Accesso al documento

10.1007/978-3-319-25524-8_18

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title = "A micro study on the evolution of arguments in amazon.Com{\textquoteright}s reviews",

abstract = "In this work we present an exploratory study on arguments in Amazon.com reviews. We manually extract positive (in favour of purchase) and negative (against it) arguments from each review concerning a selected product. Moreover, we link arguments to the rating score and length of reviews. For instance, we show that negative arguments are quite sparse during the first steps of such social review-process, while positive arguments are more equally distributed. In addition, we connect arguments through attacks and we compute Dung{\textquoteright}s extensions to check whether they capture such evolution through time. We also use Preference-based Argumentation to exploit the number of appearances of each argument in reviews.",

keywords = "consumer behavior, computational argumentation, agent-based models, consumer behavior, computational argumentation, agent-based models",

author = "Simone Gabbriellini and Francesco Santini",

year = "2015",

doi = "10.1007/978-3-319-25524-8\_18",

language = "English",

isbn = "9783319255231",

volume = "9387",

series = "LECTURE NOTES IN COMPUTER SCIENCE",

pages = "284--300",

booktitle = "PRIMA 2015: Principles and Practice of Multi-Agent Systems",

note = "18th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA) ; Conference date: 26-06-2015 Through 30-06-2015",

}

Gabbriellini, S & Santini, F 2015, A micro study on the evolution of arguments in amazon.Com’s reviews. in PRIMA 2015: Principles and Practice of Multi-Agent Systems. vol. 9387, LECTURE NOTES IN COMPUTER SCIENCE, pagg. 284-300, 18th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA), Bertinoro (FC), Italy, 26/6/15. https://doi.org/10.1007/978-3-319-25524-8_18

TY - GEN

T1 - A micro study on the evolution of arguments in amazon.Com’s reviews

AU - Gabbriellini, Simone

AU - Santini, Francesco

PY - 2015

Y1 - 2015

N2 - In this work we present an exploratory study on arguments in Amazon.com reviews. We manually extract positive (in favour of purchase) and negative (against it) arguments from each review concerning a selected product. Moreover, we link arguments to the rating score and length of reviews. For instance, we show that negative arguments are quite sparse during the first steps of such social review-process, while positive arguments are more equally distributed. In addition, we connect arguments through attacks and we compute Dung’s extensions to check whether they capture such evolution through time. We also use Preference-based Argumentation to exploit the number of appearances of each argument in reviews.

AB - In this work we present an exploratory study on arguments in Amazon.com reviews. We manually extract positive (in favour of purchase) and negative (against it) arguments from each review concerning a selected product. Moreover, we link arguments to the rating score and length of reviews. For instance, we show that negative arguments are quite sparse during the first steps of such social review-process, while positive arguments are more equally distributed. In addition, we connect arguments through attacks and we compute Dung’s extensions to check whether they capture such evolution through time. We also use Preference-based Argumentation to exploit the number of appearances of each argument in reviews.

KW - consumer behavior

KW - computational argumentation

KW - agent-based models

KW - consumer behavior

KW - computational argumentation

KW - agent-based models

UR - http://hdl.handle.net/10807/299898

U2 - 10.1007/978-3-319-25524-8_18

DO - 10.1007/978-3-319-25524-8_18

M3 - Conference contribution

SN - 9783319255231

VL - 9387

T3 - LECTURE NOTES IN COMPUTER SCIENCE

SP - 284

EP - 300

BT - PRIMA 2015: Principles and Practice of Multi-Agent Systems

T2 - 18th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)

Y2 - 26 June 2015 through 30 June 2015

ER -

A micro study on the evolution of arguments in amazon.Com’s reviews

Abstract

Serie di pubblicazioni

Convegno

OSS delle Nazioni Unite

Keywords

Accesso al documento

Altri file e collegamenti

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