Please use this identifier to cite or link to this item: 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
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INTELIGENCIA ARTIFICIAL
Versión aceptada
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