A CCG-based Approach to Fine-Grained Sentiment Analysis in Microtext

Phillip Smith; M. Lee

A CCG-based Approach to Fine-Grained Sentiment Analysis in Microtext

Phillip Smith, M. Lee

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Abstract

In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in microtext. We develop a method that makes use of the notion put forward by Ortony, Clore, and Collins (1988), that emotions are valenced reactions. This hypothesis sits central to our system, in which we adapt contextual valence shifters to infer the emotional content of a text. We integrate this with an augmented version of WordNet-Affect, which acts as our lexicon. Finally, we experiment with a corpus of headlines proposed in the 2007 SemEval Affective Task (Strapparava and Mihalcea 2007) as our microtext corpus, and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.

Original language	English
Title of host publication	PROCEEDINGS OF THE TWENTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Place of Publication	US
Publisher	AAAI
Pages	80-86
ISBN (Print)	978-1-57735-615-8
Publication status	Published - 2013
Event	AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE - Washington, United States Duration: 14 Jul 2013 → 18 Jul 2013

Conference

Conference	AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Country/Territory	United States
Period	14/07/13 → 18/07/13

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title = "A CCG-based Approach to Fine-Grained Sentiment Analysis in Microtext",

abstract = "In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in microtext. We develop a method that makes use of the notion put forward by Ortony, Clore, and Collins (1988), that emotions are valenced reactions. This hypothesis sits central to our system, in which we adapt contextual valence shifters to infer the emotional content of a text. We integrate this with an augmented version of WordNet-Affect, which acts as our lexicon. Finally, we experiment with a corpus of headlines proposed in the 2007 SemEval Affective Task (Strapparava and Mihalcea 2007) as our microtext corpus, and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.",

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T1 - A CCG-based Approach to Fine-Grained Sentiment Analysis in Microtext

AU - Smith, Phillip

AU - Lee, M.

N1 - The full text is currently unavailable on the repository

PY - 2013

Y1 - 2013

N2 - In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in microtext. We develop a method that makes use of the notion put forward by Ortony, Clore, and Collins (1988), that emotions are valenced reactions. This hypothesis sits central to our system, in which we adapt contextual valence shifters to infer the emotional content of a text. We integrate this with an augmented version of WordNet-Affect, which acts as our lexicon. Finally, we experiment with a corpus of headlines proposed in the 2007 SemEval Affective Task (Strapparava and Mihalcea 2007) as our microtext corpus, and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.

AB - In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in microtext. We develop a method that makes use of the notion put forward by Ortony, Clore, and Collins (1988), that emotions are valenced reactions. This hypothesis sits central to our system, in which we adapt contextual valence shifters to infer the emotional content of a text. We integrate this with an augmented version of WordNet-Affect, which acts as our lexicon. Finally, we experiment with a corpus of headlines proposed in the 2007 SemEval Affective Task (Strapparava and Mihalcea 2007) as our microtext corpus, and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.

UR - https://www.scopus.com/pages/publications/84883298798

M3 - Chapter

SN - 978-1-57735-615-8

SP - 80

EP - 86

BT - PROCEEDINGS OF THE TWENTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE

PB - AAAI

CY - US

T2 - AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE

Y2 - 14 July 2013 through 18 July 2013

ER -

A CCG-based Approach to Fine-Grained Sentiment Analysis in Microtext

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