Sentic API: A common-sense based API for concept-level sentiment analysis

Erik Cambria; Alexander Gelbukh; Soujanya Poria; Kenneth Kwok

Sentic API: A common-sense based API for concept-level sentiment analysis

Erik Cambria, Alexander Gelbukh, Soujanya Poria, Kenneth Kwok

Research output: Contribution to journal › Conference article › peer-review

Abstract

The bag-of-concepts model can represent semantics associated with natural language text much better than bags-of-words. In the bagof- words model, in fact, a concept such as cloud computing would be split into two separate words, disrupting the semantics of the input sentence. Working at concept-level is important for tasks such as opinion mining, especially in the case of microblogging analysis. In this work, we present Sentic API, a common-sense based application programming interface for concept-level sentiment analysis, which provides semantics and sentics (that is, denotative and connotative information) associated with 15,000 natural language concepts.

Original language	English
Pages (from-to)	19-24
Number of pages	6
Journal	CEUR Workshop Proceedings
Volume	1141
State	Published - 2014
Externally published	Yes
Event	4th Workshop on Making Sense of Microposts, #Microposts 2014, at the 23rd International Conference on the World Wide Web, WWW 2014 - Seoul, Korea, Republic of Duration: 7 Apr 2014 → 7 Apr 2014

Keywords

Natural language processing
Sentiment analysis

Cite this

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title = "Sentic API: A common-sense based API for concept-level sentiment analysis",

abstract = "The bag-of-concepts model can represent semantics associated with natural language text much better than bags-of-words. In the bagof- words model, in fact, a concept such as cloud computing would be split into two separate words, disrupting the semantics of the input sentence. Working at concept-level is important for tasks such as opinion mining, especially in the case of microblogging analysis. In this work, we present Sentic API, a common-sense based application programming interface for concept-level sentiment analysis, which provides semantics and sentics (that is, denotative and connotative information) associated with 15,000 natural language concepts.",

keywords = "Natural language processing, Sentiment analysis",

author = "Erik Cambria and Alexander Gelbukh and Soujanya Poria and Kenneth Kwok",

year = "2014",

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volume = "1141",

pages = "19--24",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

note = "4th Workshop on Making Sense of Microposts, #Microposts 2014, at the 23rd International Conference on the World Wide Web, WWW 2014 ; Conference date: 07-04-2014 Through 07-04-2014",

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TY - JOUR

T1 - Sentic API

T2 - 4th Workshop on Making Sense of Microposts, #Microposts 2014, at the 23rd International Conference on the World Wide Web, WWW 2014

AU - Cambria, Erik

AU - Gelbukh, Alexander

AU - Poria, Soujanya

AU - Kwok, Kenneth

PY - 2014

Y1 - 2014

N2 - The bag-of-concepts model can represent semantics associated with natural language text much better than bags-of-words. In the bagof- words model, in fact, a concept such as cloud computing would be split into two separate words, disrupting the semantics of the input sentence. Working at concept-level is important for tasks such as opinion mining, especially in the case of microblogging analysis. In this work, we present Sentic API, a common-sense based application programming interface for concept-level sentiment analysis, which provides semantics and sentics (that is, denotative and connotative information) associated with 15,000 natural language concepts.

AB - The bag-of-concepts model can represent semantics associated with natural language text much better than bags-of-words. In the bagof- words model, in fact, a concept such as cloud computing would be split into two separate words, disrupting the semantics of the input sentence. Working at concept-level is important for tasks such as opinion mining, especially in the case of microblogging analysis. In this work, we present Sentic API, a common-sense based application programming interface for concept-level sentiment analysis, which provides semantics and sentics (that is, denotative and connotative information) associated with 15,000 natural language concepts.

KW - Natural language processing

KW - Sentiment analysis

UR - http://www.scopus.com/inward/record.url?scp=84922017789&partnerID=8YFLogxK

M3 - Artículo de la conferencia

AN - SCOPUS:84922017789

SN - 1613-0073

VL - 1141

SP - 19

EP - 24

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 7 April 2014 through 7 April 2014

ER -

Sentic API: A common-sense based API for concept-level sentiment analysis

Abstract

Keywords

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