Learning Sentiment Lexicons in Spanish

By Verónica Pérez-Rosas, Carmen Banea, Rada Mihalcea. 2012

194 citations recorded by the source corpus.

In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90%) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74%) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66% in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources.

Subfields: Artificial Intelligence

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Citations in the corpus

Attention-based LSTM for Aspect-level Sentiment Classification (2016, background): “With the abundance of sentiment lexicons (Rao and Ravichandran, 2009; Perez-Rosas et al., 2012; Kaji and Kitsuregawa, 2007), the lexicon-based features were built for sentiment analysis (Mohammad et al.”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “5K full strength terms from the Spanish sentiment lexicon χ (Perez-Rosas et al., 2012); • 5K terms from the Russian sentiment lexicon χ (Chetviorkin and Loukachevitch, 2012).”
Interactive Attention Networks for Aspect-Level Sentiment Classification (2017, unknown): “Traditional approaches mainly focus on designing a set of features such as bag-of-words, sentiment lexicon to train a classiﬁer (e.g., SVM) for aspect-level sentiment classiﬁcation [Jiang et al. , 2011; Perez-Rosas et al. , 2012].”