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

SCHOOL OF COMPUTER ENGINEERING

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

Keywords

Detection of intention
Short texts
Text classification
Twitter

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

Cite this

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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.
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

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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

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

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

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Keywords

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