Abstract
We propose a cross-lingual distributional model to build sentiment lexicons in many languages from resources available in English. We evaluate this method for two languages, German and Turkish, and on several datasets. We show that the sentiment lexicons built using our method remarkably improve the performance of a state-of-the-art lexicon-based BiLSTM sentiment classifier.
This work was supported by TÜBİTAK Grant No. EEEAG-115E440. First author was supported by SFB991 as a SToRE visiting fellow. We acknowledge the support of NVIDIA Corporation with the donation of a GPU used for this research.
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The constructed lexicons are available at https://github.com/nbehzad/CLSL.
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Naderalvojoud, B., Qasemizadeh, B., Kallmeyer, L., Sezer, E.A. (2018). A Cross-Lingual Approach for Building Multilingual Sentiment Lexicons. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_28
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