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Abstract:
We propose the weakly supervised \emph{Multi-Experts Model} (MEM) for
analyzing the semantic orientation of opinions expressed in natural language
reviews. In contrast to most prior work, MEM predicts both opinion polarity and
opinion strength at the level of individual sentences; such fine-grained
analysis helps to understand better why users like or dislike the entity under
review. A key challenge in this setting is that it is hard to obtain
sentence-level training data for both polarity and strength. For this reason,
MEM is weakly supervised: It starts with potentially noisy indicators obtained
from coarse-grained training data (i.e., document-level ratings), a small set
of diverse base predictors, and, if available, small amounts of fine-grained
training data. We integrate these noisy indicators into a unified probabilistic
framework using ideas from ensemble learning and graph-based semi-supervised
learning. Our experiments indicate that MEM outperforms state-of-the-art
methods by a significant margin.
