  Aspect classification, identifying aspects of text segments, facilitates
numerous applications, such as sentiment analysis and review summarization. To
alleviate the human effort on annotating massive texts, in this paper, we study
the problem of classifying aspects based on only a few user-provided seed words
for pre-defined aspects. The major challenge lies in how to handle the noisy
misc aspect, which is designed for texts without any pre-defined aspects. Even
domain experts have difficulties to nominate seed words for the misc aspect,
making existing seed-driven text classification methods not applicable. We
propose a novel framework, ARYA, which enables mutual enhancements between
pre-defined aspects and the misc aspect via iterative classifier training and
seed updating. Specifically, it trains a classifier for pre-defined aspects and
then leverages it to induce the supervision for the misc aspect. The prediction
results of the misc aspect are later utilized to filter out noisy seed words
for pre-defined aspects. Experiments in two domains demonstrate the superior
performance of our proposed framework, as well as the necessity and importance
of properly modeling the misc aspect.
