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 - Funding Information:
This work was done under partial support of the Mexican Government (CONACYT-134186, CONACYT grant #308719, SNI, COFAA-IPN, SIP-IPN 20144274) and FP7-PEOPLE-2010-IRSES: “Web Information Quality Evaluation Initiative (WIQ-EI)” European Commission project 269180.
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
UR - http://www.scopus.com/inward/record.url?scp=84921655014&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-12027-0_10
DO - 10.1007/978-3-319-12027-0_10
M3 - Article
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 -