Multilingual Subjectivity Analysis Using Machine Translation

By Carmen Banea, Rada Mihalcea, Janyce Wiebe, Samer Hassan. Conference on Empirical Methods in Natural Language Processing, 2008

320 citations recorded by the source corpus.

Although research in other languages is increasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic resources and tools that are available for this language. In this paper, we propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. Specifically, we attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages. Through comparative evaluations on two different languages (Romanian and Spanish), we show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.

Subfields: Artificial Intelligence, Food Science

View Paper: https://www.semanticscholar.org/paper/1cd968653a74678176ebf7433b4032204dbf5526

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Food Science

Citations in the corpus

Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, methodology): “These two basic corpus-based methods have been used in Banea et al. (2008) for Romanian subjectivity analysis.”
Does ‘well-being’ translate on Twitter? (2016, background): “, 2007) and English and both Spanish and Romanian (Banea et al., 2008) based on the English MPQA sentiment data suggest that, in spite of word ambiguity in either the source or target language, automatic translation is a viable alternative to the construction of models in target languages.”
A review of natural language processing techniques for opinion mining systems (2017, methodology): “it possible to exploit the abundant English resources [26, 27, 28, 29].”
Subjective Natural Language Problems: Motivations, Applications, Characterizations, and Implications (2011, methodology): “Banea et al already noted: “We have seen a surge in interest towards the application of automatic tools and techniques for the extraction of opinions, emotions, and sentiments in text ( subjectivity )” (p. 127) (Banea et al, 2008).”
Sentiment Analysis: An Overview from Linguistics (2016, unknown): “In some cases, the focus is a combination of different languages (Banea et al 2014, Banea et al 2008, Mihalcea et al 2007, Popat et al 2013).”
Co-Training for Cross-Lingual Sentiment Classification (2009, methodology): “The above two methods have been used in (Banea et al., 2008) for Romanian subjectivity analysis, but the experimental results are not very promising.”
Learning Sentiment Lexicons in Spanish (2012, background): “…is growing, including Japanese (Kobayashi et al., 2004; Suzuki et al., 2006; Takamura et al., 2006; Kanayama and Nasukawa, 2006), Chinese (Hu et al., 2005; Tsou et al., 2005; Zagibalov and Carroll, 2008), German (Kim and Hovy, 2006), and Romanian (Mihalcea et al., 2007; Banea et al., 2008b).”