Abstract
In sentiment detection, the natural language
processing community has focused on determining holders, facets, and valences, but has
paid little attention to the reasons for sentiment decisions. Our work considers human
motives as the driver for human sentiments
and addresses the problem of motive detection as the first step. Following a study in
psychology, we define six basic motives that
cover a wide range of topics appearing in review texts, annotate 1,600 texts in restaurant
and laptop domains with the motives, and report the performance of baseline methods on
this new dataset. We also show that crossdomain transfer learning boosts detection performance, which indicates that these universal
motives exist across different domains.