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

Helena Gómez-Adorno; David Pinto; Manuel Montes; Grigori Sidorov; Rodrigo Alfaro

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 Alfaro

Centro de Investigación en Computación (CIC)

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

6 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Páginas (desde-hasta)	120-128
Número de páginas	9
Publicación	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	8864
DOI	https://doi.org/10.1007/978-3-319-12027-0_10
Estado	Publicada - 1 ene. 2014

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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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Content and style features for automatic detection of users’ intentions in tweets. / Gómez-Adorno, Helena; Pinto, David; Montes, Manuel et al.
En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8864, 01.01.2014, p. 120-128.

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

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, Rodrigo

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

PY - 2014/1/1

Y1 - 2014/1/1

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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U2 - 10.1007/978-3-319-12027-0_10

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

M3 - Artículo

AN - SCOPUS:84921655014

SN - 0302-9743

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)

ER -

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

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