An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure
This addresses the challenge of costly labeled data and feature engineering in aspect-based sentiment analysis, though it is incremental as it builds on existing unsupervised techniques.
The paper tackles the problem of aspect category detection in sentiment analysis by proposing an unsupervised method that uses clusters of unlabeled reviews and soft cosine similarity, achieving results that outperform several baselines on the SemEval-2014 restaurant dataset.
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.