  Aspect category detection is one of the important and challenging subtasks of
aspect-based sentiment analysis. Given a set of pre-defined categories, this
task aims to detect categories which are indicated implicitly or explicitly in
a given review sentence. Supervised machine learning approaches perform well to
accomplish this subtask. Note that, the performance of these methods depends on
the availability of labeled train data, which is often difficult and costly to
obtain. Besides, most of these supervised methods require feature engineering
to perform well. In this paper, we propose an unsupervised method to address
aspect category detection task without the need for any feature engineering.
Our method utilizes clusters of unlabeled reviews and soft cosine similarity
measure to accomplish aspect category detection task. Experimental results on
SemEval-2014 restaurant dataset shows that proposed unsupervised approach
outperforms several baselines by a substantial margin.
