Generating Sentiment Lexicons for German Twitter

TL;DR

This paper compares different sentiment lexicon generation methods for German Twitter data, showing that semi-automatic translations outperform automatic methods and dictionary-based approaches are more effective than corpus-based ones.

Contribution

It systematically evaluates and compares lexicon generation approaches for German social media, highlighting the effectiveness of semi-automatic translation and dictionary-based methods.

Findings

Semi-automatic translations outperform automatic SLG methods (F1-score 0.589).

Dictionary-based techniques yield better polarity lists than corpus-based approaches.

Dictionary-based methods achieve higher F1-scores (up to 0.479) compared to corpus-based methods (up to 0.419).

Abstract

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.