Title

A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis

Author

Oscar Sánchez Siordia

Eric Tellez

SABINO MIRANDA JIMENEZ

Mario Graff

Daniela Moctezuma

Elio Atenógenes Villaseñor García

Access level

Embargoed Access

License

http://creativecommons.org/licenses/by-nc-nd/4.0

Alternative identifier

doi: https://doi.org/10.1016/j.eswa.2017.03.071

Subjects

Sentiment Analysis - (AUTOR) Error-robust text representations - (AUTOR) Opinion mining - (AUTOR) INGENIERÍA Y TECNOLOGÍA - (CTI) CIENCIAS TECNOLÓGICAS - (CTI) TECNOLOGÍA DE LOS ORDENADORES - (CTI) INTELIGENCIA ARTIFICIAL - (CTI) INTELIGENCIA ARTIFICIAL - (CTI)

Summary or description

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.

Publisher

Elsevier

Publish date

September, 2017

Publication type

Article

Publication version

Accepted Version

Information Resource

http://centrogeo.repositorioinstitucional.mx/jspui/handle/1012/243

Format

application/pdf

Source

Expert Systems with Applications Volume 81, 15 September 2017, Pages 457-471

Language

English

Audience

Students

Researchers

Teachers

Source repository

Repositorio Institucional de CENTROGEO

Downloads

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