A Case Study of Spanish Text Transformations for Twitter Sentiment Analysis
Oscar Sánchez Siordia
SABINO MIRANDA JIMENEZ
Elio Atenógenes Villaseñor García
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.
Expert Systems with Applications Volume 81, 15 September 2017, Pages 457-471
Repositorio Institucional de CENTROGEO