Generating Sentiment Lexicons for German Twitter
This work addresses the challenge of adapting sentiment analysis tools to non-English languages and noisy social media domains, providing practical insights for researchers and practitioners in NLP.
The paper tackled the problem of generating sentiment lexicons for German Twitter by comparing semi-automatic translations of English lists with automatic methods applied directly to German data, finding that semi-automatic translations achieved a macro-averaged F1-score of 0.589 and outperformed corpus-based approaches.
Despite a substantial progress made in developing new sentiment lexicon generation (SLG) methods for English, the task of transferring these approaches to other languages and domains in a sound way still remains open. In this paper, we contribute to the solution of this problem by systematically comparing semi-automatic translations of common English polarity lists with the results of the original automatic SLG algorithms, which were applied directly to German data. We evaluate these lexicons on a corpus of 7,992 manually annotated tweets. In addition to that, we also collate the results of dictionary- and corpus-based SLG methods in order to find out which of these paradigms is better suited for the inherently noisy domain of social media. Our experiments show that semi-automatic translations notably outperform automatic systems (reaching a macro-averaged F1-score of 0.589), and that dictionary-based techniques produce much better polarity lists as compared to corpus-based approaches (whose best F1-scores run up to 0.479 and 0.419 respectively) even for the non-standard Twitter genre.