  We tackle the tasks of automatically identifying comparative sentences and
categorizing the intended preference (e.g., "Python has better NLP libraries
than MATLAB" => (Python, better, MATLAB). To this end, we manually annotate
7,199 sentences for 217 distinct target item pairs from several domains (27% of
the sentences contain an oriented comparison in the sense of "better" or
"worse"). A gradient boosting model based on pre-trained sentence embeddings
reaches an F1 score of 85% in our experimental evaluation. The model can be
used to extract comparative sentences for pro/con argumentation in comparative
/ argument search engines or debating technologies.
