A machine learning approach to sentiment analysis in multilingual Web texts

By Erik Boiy, Marie-Francine Moens. Information retrieval (Boston), 2009

489 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/462754e479123ef2167c3946fd4cd04a242c2767

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In Artificial Intelligence

Citations in the corpus

Sentiment analysis: A review and comparative analysis of web services (2015, background): “It seems a simple task; however, it is a really complex task, especially when opinions come from multiple domains or languages [76,30,10].”
Sentiment classification: The contribution of ensemble learning (2014, background): “Since the late 1990s, sentiment classification has been a hot research topic in the areas of data mining, information retrieval, and natural language processing [4] [28].”
A survey on classification techniques for opinion mining and sentiment analysis (2017, unknown): “…(2011) 64 Li and Liu (2012) 77 Pang and Lee (2004) Penalver-Martinez et al. (2014) 89.6 Fernández-Gavilanes et al. (2016) 69.95 Fersini et al. (2016) 81.7 Saleh et al. (2011) 85.35 Boiy and Moens (2009) 87.40 Blitzer et al. (2007) Xia et al. (2016) 80 Xia et al. (2011) 85.58 Poria et al. (2014) 87”
More than words: Social networks' text mining for consumer brand sentiments (2013, background): “For instance, Boiy and Moens (2009) provide an excellent example by showing that the part of the sentence that follows ‘‘même si/even if’’ in a French sentence expresses the least affective feeling ‘‘[même si le film a eu beaucoup de succès, je le trouvais vraiment nul!/even though the movie had a l”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “Boiy and Moens [46] performed multilingual and multiple domain sentiment classification.”
Aspect-Oriented Opinion Mining from User Reviews in Croatian (2013, background): “…Klenner, 2008; Qiu et al., 2009; Choi et al., 2009) to cross-domain approaches (Wilson et al., 2009; Taboada et al., 2011), and from lexicon-based methods (Popescu and Etzioni, 2007; Jijkoun et al., 2010; Taboada et al., 2011) to machine learning approaches (Boiy and Moens, 2009; Go et al., 2009).”