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

An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure