Attitude sensing in text based on a compositional linguistic approach

Alena Neviarouskaya, Helmut Prendinger, Mitsuru Ishizuka

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this article, we introduce a compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our attitude analysis model (@AM) from other systems. First, our method classifies sentences using fine-grained attitude labels (nine for affective states, two for positive and negative judgment, and two for positive and negative appreciation), as compared against other methods that mainly focus on two sentiment categories (positive and negative) or basic emotions. Next, our @AM is based on the analysis of syntactic and dependence relations between words in a sentence, the compositionality principle, a novel linguistic approach based on the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. As distinct from the state-of-the-art approaches, the proposed method extensively deals with the semantics of terms, processes sentences of different complexity, handles not only correctly written text but also informal messages, and encodes the strength and the level of confidence of attitude through numerical values. The performance of our @AM was evaluated on data sets represented by sentences from different domains. @AM achieved a high level of accuracy on sentences from personal stories about life experiences, fairy tales, and news headlines, outperforming other methods on several measures.

Original languageEnglish
Pages (from-to)256-300
Number of pages45
JournalComputational Intelligence
Volume31
Issue number2
DOIs
Publication statusPublished - 2015 May 1
Externally publishedYes

Fingerprint

Linguistics
Sensing
Syntactics
Labels
Semantics
Distinct
Compositionality
Model Analysis
Confidence
Classify
Text
Term
Narrative

Keywords

  • affective computing
  • attitude analysis in text
  • compositional linguistic approach
  • intelligent analytical interface

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Mathematics

Cite this

Attitude sensing in text based on a compositional linguistic approach. / Neviarouskaya, Alena; Prendinger, Helmut; Ishizuka, Mitsuru.

In: Computational Intelligence, Vol. 31, No. 2, 01.05.2015, p. 256-300.

Research output: Contribution to journalArticle

Neviarouskaya, A, Prendinger, H & Ishizuka, M 2015, 'Attitude sensing in text based on a compositional linguistic approach', Computational Intelligence, vol. 31, no. 2, pp. 256-300. https://doi.org/10.1111/coin.12020
Neviarouskaya, Alena ; Prendinger, Helmut ; Ishizuka, Mitsuru. / Attitude sensing in text based on a compositional linguistic approach. In: Computational Intelligence. 2015 ; Vol. 31, No. 2. pp. 256-300.
@article{81d140a728224fe4ab435cf41295a9c7,
title = "Attitude sensing in text based on a compositional linguistic approach",
abstract = "In this article, we introduce a compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our attitude analysis model (@AM) from other systems. First, our method classifies sentences using fine-grained attitude labels (nine for affective states, two for positive and negative judgment, and two for positive and negative appreciation), as compared against other methods that mainly focus on two sentiment categories (positive and negative) or basic emotions. Next, our @AM is based on the analysis of syntactic and dependence relations between words in a sentence, the compositionality principle, a novel linguistic approach based on the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. As distinct from the state-of-the-art approaches, the proposed method extensively deals with the semantics of terms, processes sentences of different complexity, handles not only correctly written text but also informal messages, and encodes the strength and the level of confidence of attitude through numerical values. The performance of our @AM was evaluated on data sets represented by sentences from different domains. @AM achieved a high level of accuracy on sentences from personal stories about life experiences, fairy tales, and news headlines, outperforming other methods on several measures.",
keywords = "affective computing, attitude analysis in text, compositional linguistic approach, intelligent analytical interface",
author = "Alena Neviarouskaya and Helmut Prendinger and Mitsuru Ishizuka",
year = "2015",
month = "5",
day = "1",
doi = "10.1111/coin.12020",
language = "English",
volume = "31",
pages = "256--300",
journal = "Computational Intelligence",
issn = "0824-7935",
publisher = "Wiley-Blackwell",
number = "2",

}

TY - JOUR

T1 - Attitude sensing in text based on a compositional linguistic approach

AU - Neviarouskaya, Alena

AU - Prendinger, Helmut

AU - Ishizuka, Mitsuru

PY - 2015/5/1

Y1 - 2015/5/1

N2 - In this article, we introduce a compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our attitude analysis model (@AM) from other systems. First, our method classifies sentences using fine-grained attitude labels (nine for affective states, two for positive and negative judgment, and two for positive and negative appreciation), as compared against other methods that mainly focus on two sentiment categories (positive and negative) or basic emotions. Next, our @AM is based on the analysis of syntactic and dependence relations between words in a sentence, the compositionality principle, a novel linguistic approach based on the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. As distinct from the state-of-the-art approaches, the proposed method extensively deals with the semantics of terms, processes sentences of different complexity, handles not only correctly written text but also informal messages, and encodes the strength and the level of confidence of attitude through numerical values. The performance of our @AM was evaluated on data sets represented by sentences from different domains. @AM achieved a high level of accuracy on sentences from personal stories about life experiences, fairy tales, and news headlines, outperforming other methods on several measures.

AB - In this article, we introduce a compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our attitude analysis model (@AM) from other systems. First, our method classifies sentences using fine-grained attitude labels (nine for affective states, two for positive and negative judgment, and two for positive and negative appreciation), as compared against other methods that mainly focus on two sentiment categories (positive and negative) or basic emotions. Next, our @AM is based on the analysis of syntactic and dependence relations between words in a sentence, the compositionality principle, a novel linguistic approach based on the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. As distinct from the state-of-the-art approaches, the proposed method extensively deals with the semantics of terms, processes sentences of different complexity, handles not only correctly written text but also informal messages, and encodes the strength and the level of confidence of attitude through numerical values. The performance of our @AM was evaluated on data sets represented by sentences from different domains. @AM achieved a high level of accuracy on sentences from personal stories about life experiences, fairy tales, and news headlines, outperforming other methods on several measures.

KW - affective computing

KW - attitude analysis in text

KW - compositional linguistic approach

KW - intelligent analytical interface

UR - http://www.scopus.com/inward/record.url?scp=84929047138&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929047138&partnerID=8YFLogxK

U2 - 10.1111/coin.12020

DO - 10.1111/coin.12020

M3 - Article

AN - SCOPUS:84929047138

VL - 31

SP - 256

EP - 300

JO - Computational Intelligence

JF - Computational Intelligence

SN - 0824-7935

IS - 2

ER -