Generating Sentiment Lexicons for German Twitter
Table 1: Evaluation of semi-automatic German sentiment lexicons. GPC – German Polarity Clues [Waltinger2010], SWS – SentiWS [Remus et al.2010], ZPL – Zurich Polarity Lexicon [Clematide and Klenner2010]
['[BOLD] Lexicon', '[BOLD] Positive Expressions Precision', '[BOLD] Positive Expressions Recall', '[BOLD] Positive Expressions  [ITALIC] F1', '[BOLD] Negative Expressions Precision', '[BOLD] Negative Expressions Recall', '[BOLD] Negative Expressions  [ITALIC] F1', '[BOLD] Neutral Terms Precision', '[BOLD] Neutral Terms Recall', '[BOLD] Neutral Terms  [ITALIC] F1', '[BOLD] Macro  [ITALIC] F1', '[BOLD] Micro  [ITALIC] F1']
For our evaluation, we tested the three lexicons in isolation and also built their union and intersection in order to check for “synergy” effects. As can be seen from the statistics, with a few exceptions, the highest scores for all classes as well as the best macro- and micro-averaged F1-measures are achieved by the intersection of all three lexicons. On the other hand, as expected, the highest recall of polar expressions (and consequently the best precision at recognizing neutral terms) is attained by the union of these resources. The only case where individual lexicons are able to outperform these combinations is observed for the F1-score of the negative class, where both SentiWS and ZPL show better results than their intersection, which is mainly due to the higher recall of these two polarity lists.