Building sentiment Lexicons applying graph theory on information from three Norwegian thesauruses
Journal article, Peer reviewed
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Original versionHammer, H., Bai, A., Yazidi, A., & Engelstad, P. (2014). Building sentiment Lexicons applying graph theory on information from three Norwegian thesauruses. Norsk Informatikkonferanse (NIK). http://ojs.bibsys.no/index.php/NIK/article/view/20
Sentiment lexicons are the most used tool to automatically predict sentiment in text. To the best of our knowledge, there exist no openly available sentiment lexicons for the Norwegian language. Thus in this paper we applied two different strategies to automatically generate sentiment lexicons for the Norwegian language. The first strategy used machine translation to translate an English sentiment lexicon to Norwegian and the other strategy used information from three different thesauruses to build several sentiment lexicons. The lexicons based on thesauruses were built using the Label propagation algorithm from graph theory. The lexicons were evaluated by classifying product and movie reviews. The results show satisfying classification performances. Different sentiment lexicons perform well on product and on movie reviews. Overall the lexicon based on machine translation performed the best, showing that linguistic resources in English can be translated to Norwegian without losing significant value.