Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-12027-0_10 | ADVANCES IN ARTIFICIAL INTELLIGENCE (IBERAMIA 2014) |
Keywords | Field | DocType |
Short texts,Text classification,Twitter,Detection of intention | World Wide Web,Information retrieval,Computer science | Conference |
Volume | ISSN | Citations |
8864 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helena Gómez-Adorno | 1 | 40 | 16.01 |
| David Pinto | 2 | 280 | 35.77 |
| Manuel Montes-Y-Gómez | 3 | 638 | 83.97 |
| Grigori Sidorov | 4 | 398 | 60.51 |
| Rodrigo Alfaro | 5 | 1 | 1.39 |