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http://centrogeo.repositorioinstitucional.mx/jspui/handle/1012/243| A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis | |
| Oscar Sánchez Siordia Eric Tellez SABINO MIRANDA JIMENEZ Mario Graff Daniela Moctezuma Elio Atenógenes Villaseñor García | |
| En Embargo | |
| 16-10-2019 | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1016/j.eswa.2017.03.071 | |
| Sentiment Analysis Error-robust text representations Opinion mining | |
| Sentiment analysis is a text mining task that determines the polarity of a given text, i.e., its positiveness or negativeness. Recently, it has received a lot of attention given the interest in opinion mining in micro-blogging platforms. These new forms of textual expressions present new challenges to analyze text because of the use of slang, orthographic and grammatical errors, among others. Along with these challenges, a practical sentiment classifier should be able to handle efficiently large workloads. The aim of this research is to identify in a large set of combinations which text transformations (lemmatization, stemming, entity removal, among others), tokenizers (e.g., word n-grams), and token-weighting schemes make the most impact on the accuracy of a classifier (Support Vector Machine) trained on two Spanish datasets. The methodology used is to exhaustively analyze all combinations of text transformations and their respective parameters to find out what common characteristics the best performing classifiers have. Furthermore, we introduce a novel approach based on the combination of word-based n-grams and character-based q-grams. The results show that this novel combination of words and characters produces a classifier that outperforms the traditional wordbased combination by 11.17% and 5.62% on the INEGI and TASS’15 dataset, respectively. | |
| Elsevier | |
| 2017-09 | |
| Artículo | |
| Expert Systems with Applications Volume 81, 15 September 2017, Pages 457-471 | |
| Inglés | |
| Estudiantes Investigadores Maestros | |
| INTELIGENCIA ARTIFICIAL | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | CentroGeo |