Determining the Polarity of Words through a Common Online Dictionary

Nuno Miguel Cavalheiro Marques

doi:10.1007/978-3-642-24769-9_47

Determining the Polarity of Words through a Common Online Dictionary

Nuno Miguel Cavalheiro Marques

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Considerable attention has been given to polarity of words and the creation of large polarity lexicons. Most of the approaches rely on advanced tools like part-of-speech taggers and rich lexical resources such as WordNet. In this paper we show and examine the viability to create a moderate-sized polarity lexicon using only a common online dictionary, five positive and five negative words, a set of highly accurate extraction rules, and a simple yet effective polarity propagation algorithm. The algorithm evaluation results show an accuracy of 84.86% for a lexicon of 3034 words.

Original language	Unknown
Title of host publication	Lecture Notes in Computer Science
Editors	L Antunes, H Pinto
Publisher	Springer Berlin / Heidelberg
Pages	649-663
Volume	7026
ISBN (Print)	978-3-642-24768-2
DOIs	https://doi.org/10.1007/978-3-642-24769-9_47
Publication status	Published - 1 Jan 2011
Event	Progress in Artificial Intelligence, Procedings of the 15th Portuguese Conference on Artificial Intelligence (EPIA'11) - Duration: 1 Jan 2011 → …

Conference

Conference	Progress in Artificial Intelligence, Procedings of the 15th Portuguese Conference on Artificial Intelligence (EPIA'11)
Period	1/01/11 → …

Access to Document

10.1007/978-3-642-24769-9_47

http://dx.doi.org/10.1007/978-3-642-24769-9_47

Cite this

@inproceedings{efc883b0039d402d8c83f2c7d9a2e406,

title = "Determining the Polarity of Words through a Common Online Dictionary",

abstract = "Considerable attention has been given to polarity of words and the creation of large polarity lexicons. Most of the approaches rely on advanced tools like part-of-speech taggers and rich lexical resources such as WordNet. In this paper we show and examine the viability to create a moderate-sized polarity lexicon using only a common online dictionary, five positive and five negative words, a set of highly accurate extraction rules, and a simple yet effective polarity propagation algorithm. The algorithm evaluation results show an accuracy of 84.86% for a lexicon of 3034 words.",

author = "{Cavalheiro Marques}, {Nuno Miguel}",

note = "10.1007/978-3-642-24769-9_47; Progress in Artificial Intelligence, Procedings of the 15th Portuguese Conference on Artificial Intelligence (EPIA'11) ; Conference date: 01-01-2011",

year = "2011",

month = jan,

day = "1",

doi = "10.1007/978-3-642-24769-9_47",

language = "Unknown",

isbn = "978-3-642-24768-2",

volume = "7026",

pages = "649--663",

editor = "L Antunes and H Pinto",

booktitle = "Lecture Notes in Computer Science",

publisher = "Springer Berlin / Heidelberg",

}

Cavalheiro Marques, NM 2011, Determining the Polarity of Words through a Common Online Dictionary. in L Antunes & H Pinto (eds), Lecture Notes in Computer Science. vol. 7026, Springer Berlin / Heidelberg, pp. 649-663, Progress in Artificial Intelligence, Procedings of the 15th Portuguese Conference on Artificial Intelligence (EPIA'11), 1/01/11. https://doi.org/10.1007/978-3-642-24769-9_47

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Y1 - 2011/1/1

N2 - Considerable attention has been given to polarity of words and the creation of large polarity lexicons. Most of the approaches rely on advanced tools like part-of-speech taggers and rich lexical resources such as WordNet. In this paper we show and examine the viability to create a moderate-sized polarity lexicon using only a common online dictionary, five positive and five negative words, a set of highly accurate extraction rules, and a simple yet effective polarity propagation algorithm. The algorithm evaluation results show an accuracy of 84.86% for a lexicon of 3034 words.

AB - Considerable attention has been given to polarity of words and the creation of large polarity lexicons. Most of the approaches rely on advanced tools like part-of-speech taggers and rich lexical resources such as WordNet. In this paper we show and examine the viability to create a moderate-sized polarity lexicon using only a common online dictionary, five positive and five negative words, a set of highly accurate extraction rules, and a simple yet effective polarity propagation algorithm. The algorithm evaluation results show an accuracy of 84.86% for a lexicon of 3034 words.

U2 - 10.1007/978-3-642-24769-9_47

DO - 10.1007/978-3-642-24769-9_47

M3 - Conference contribution

SN - 978-3-642-24768-2

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