  Detecting and aggregating sentiments toward people, organizations, and events
expressed in unstructured social media have become critical text mining
operations. Early systems detected sentiments over whole passages, whereas more
recently, target-specific sentiments have been of greater interest. In this
paper, we present MTTDSC, a multi-task target-dependent sentiment
classification system that is informed by feature representation learnt for the
related auxiliary task of passage-level sentiment classification. The auxiliary
task uses a gated recurrent unit (GRU) and pools GRU states, followed by an
auxiliary fully-connected layer that outputs passage-level predictions. In the
main task, these GRUs contribute auxiliary per-token representations over and
above word embeddings. The main task has its own, separate GRUs. The auxiliary
and main GRUs send their states to a different fully connected layer, trained
for the main task. Extensive experiments using two auxiliary datasets and three
benchmark datasets (of which one is new, introduced by us) for the main task
demonstrate that MTTDSC outperforms state-of-the-art baselines. Using
word-level sensitivity analysis, we present anecdotal evidence that prior
systems can make incorrect target-specific predictions because they miss
sentiments expressed by words independent of target.
