  We propose a topic-dependent attention model for sentiment classification and
topic extraction. Our model assumes that a global topic embedding is shared
across documents and employs an attention mechanism to derive local topic
embedding for words and sentences. These are subsequently incorporated in a
modified Gated Recurrent Unit (GRU) for sentiment classification and extraction
of topics bearing different sentiment polarities. Those topics emerge from the
words' local topic embeddings learned by the internal attention of the GRU
cells in the context of a multi-task learning framework. In this paper, we
present the hierarchical architecture, the new GRU unit and the experiments
conducted on users' reviews which demonstrate classification performance on a
par with the state-of-the-art methodologies for sentiment classification and
topic coherence outperforming the current approaches for supervised topic
extraction. In addition, our model is able to extract coherent aspect-sentiment
clusters despite using no aspect-level annotations for training.
