Content and style features for automatic detection of users’ intentions in tweets

Helena Gómez-Adorno; David Pinto; Manuel Montes; Grigori Sidorov; RODRIGO MARCELO ALFARO ARANCIBIA

doi:10.1007/978-3-319-12027-0_10

Content and style features for automatic detection of users’ intentions in tweets

Helena Gómez-Adorno, David Pinto, Manuel Montes, Grigori Sidorov, RODRIGO MARCELO ALFARO ARANCIBIA

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

The aim of this paper is to evaluate the use of content and style features in automatic classification of intentions of Tweets. For this we propose different style features and evaluate them using a machine learning approach. We found that although the style features by themselves are useful for the identification of the intentions of tweets, it is better to combine such features with the content ones. We present a set of experiments, where we achieved a 9.46 % of improvement on the overall performance of the classification with the combination of content and style features as compared with the content features.

Original language	English
Pages (from-to)	120-128
Number of pages	9
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8864
DOIs	https://doi.org/10.1007/978-3-319-12027-0_10
State	Published - 2014
Externally published	Yes

Keywords

Detection of intention
Short texts
Text classification
Twitter

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Gómez-Adorno, H., Pinto, D., Montes, M., Sidorov, G., & ALFARO ARANCIBIA, RODRIGO. MARCELO. (2014). Content and style features for automatic detection of users’ intentions in tweets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8864, 120-128. https://doi.org/10.1007/978-3-319-12027-0_10

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abstract = "The aim of this paper is to evaluate the use of content and style features in automatic classification of intentions of Tweets. For this we propose different style features and evaluate them using a machine learning approach. We found that although the style features by themselves are useful for the identification of the intentions of tweets, it is better to combine such features with the content ones. We present a set of experiments, where we achieved a 9.46 % of improvement on the overall performance of the classification with the combination of content and style features as compared with the content features.",

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author = "Helena G{\'o}mez-Adorno and David Pinto and Manuel Montes and Grigori Sidorov and {ALFARO ARANCIBIA}, {RODRIGO MARCELO}",

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doi = "10.1007/978-3-319-12027-0_10",

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Gómez-Adorno, H, Pinto, D, Montes, M, Sidorov, G & ALFARO ARANCIBIA, RODRIGOMARCELO 2014, 'Content and style features for automatic detection of users’ intentions in tweets', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8864, pp. 120-128. https://doi.org/10.1007/978-3-319-12027-0_10

Content and style features for automatic detection of users’ intentions in tweets. / Gómez-Adorno, Helena; Pinto, David; Montes, Manuel; Sidorov, Grigori; ALFARO ARANCIBIA, RODRIGO MARCELO.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8864, 2014, p. 120-128.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Content and style features for automatic detection of users’ intentions in tweets

AU - Gómez-Adorno, Helena

AU - Pinto, David

AU - Montes, Manuel

AU - Sidorov, Grigori

AU - ALFARO ARANCIBIA, RODRIGO MARCELO

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - The aim of this paper is to evaluate the use of content and style features in automatic classification of intentions of Tweets. For this we propose different style features and evaluate them using a machine learning approach. We found that although the style features by themselves are useful for the identification of the intentions of tweets, it is better to combine such features with the content ones. We present a set of experiments, where we achieved a 9.46 % of improvement on the overall performance of the classification with the combination of content and style features as compared with the content features.

AB - The aim of this paper is to evaluate the use of content and style features in automatic classification of intentions of Tweets. For this we propose different style features and evaluate them using a machine learning approach. We found that although the style features by themselves are useful for the identification of the intentions of tweets, it is better to combine such features with the content ones. We present a set of experiments, where we achieved a 9.46 % of improvement on the overall performance of the classification with the combination of content and style features as compared with the content features.

KW - Detection of intention

KW - Short texts

KW - Text classification

KW - Twitter

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DO - 10.1007/978-3-319-12027-0_10

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AN - SCOPUS:84921655014

VL - 8864

SP - 120

EP - 128

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -

Content and style features for automatic detection of users’ intentions in tweets

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Keywords

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