Detecting expressions of blame or praise in text

Udochukwu Orizu, Yulan He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.
Original languageEnglish
Title of host publicationThe 10th edition of the Language Resources and Evaluation Conference (LREC)
EditorsNicoletta Calzolari, Khalid Choukri, Thierry Declerck, et al
Pages4124-4129
Number of pages6
ISBN (Electronic)978-2-9517408-9-1
Publication statusPublished - 28 May 2016
Event10th Language Resources and Evaluation Conference: LREC 2016 - Portorož, Slovenia
Duration: 23 May 201628 May 2016

Conference

Conference10th Language Resources and Evaluation Conference
CountrySlovenia
CityPortorož
Period23/05/1628/05/16

Fingerprint

Classifiers
Intelligent agents

Bibliographical note

-The LREC 2016 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Keywords

  • blame/praise detection
  • text classification
  • social computing

Cite this

Orizu, U., & He, Y. (2016). Detecting expressions of blame or praise in text. In N. Calzolari, K. Choukri, T. Declerck, & et al (Eds.), The 10th edition of the Language Resources and Evaluation Conference (LREC) (pp. 4124-4129)
Orizu, Udochukwu ; He, Yulan. / Detecting expressions of blame or praise in text. The 10th edition of the Language Resources and Evaluation Conference (LREC). editor / Nicoletta Calzolari ; Khalid Choukri ; Thierry Declerck ; et al. 2016. pp. 4124-4129
@inproceedings{43015cb0c2b4488baf418baf25291b7b,
title = "Detecting expressions of blame or praise in text",
abstract = "The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.",
keywords = "blame/praise detection, text classification, social computing",
author = "Udochukwu Orizu and Yulan He",
note = "-The LREC 2016 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.",
year = "2016",
month = "5",
day = "28",
language = "English",
isbn = "978-2-9517408-9-1",
pages = "4124--4129",
editor = "Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and {et al}",
booktitle = "The 10th edition of the Language Resources and Evaluation Conference (LREC)",

}

Orizu, U & He, Y 2016, Detecting expressions of blame or praise in text. in N Calzolari, K Choukri, T Declerck & et al (eds), The 10th edition of the Language Resources and Evaluation Conference (LREC). pp. 4124-4129, 10th Language Resources and Evaluation Conference, Portorož, Slovenia, 23/05/16.

Detecting expressions of blame or praise in text. / Orizu, Udochukwu; He, Yulan.

The 10th edition of the Language Resources and Evaluation Conference (LREC). ed. / Nicoletta Calzolari; Khalid Choukri; Thierry Declerck; et al. 2016. p. 4124-4129.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Detecting expressions of blame or praise in text

AU - Orizu, Udochukwu

AU - He, Yulan

N1 - -The LREC 2016 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PY - 2016/5/28

Y1 - 2016/5/28

N2 - The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.

AB - The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.

KW - blame/praise detection

KW - text classification

KW - social computing

M3 - Conference contribution

SN - 978-2-9517408-9-1

SP - 4124

EP - 4129

BT - The 10th edition of the Language Resources and Evaluation Conference (LREC)

A2 - Calzolari, Nicoletta

A2 - Choukri, Khalid

A2 - Declerck, Thierry

A2 - et al,

ER -

Orizu U, He Y. Detecting expressions of blame or praise in text. In Calzolari N, Choukri K, Declerck T, et al, editors, The 10th edition of the Language Resources and Evaluation Conference (LREC). 2016. p. 4124-4129