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

arXiv: https://arxiv.org/abs/1812.03361 · HTML: https://mlsanity.com/paper/1812.03361/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection](https://mlsanity.com/paper/2103.09519/) (2021)
- [LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features](https://mlsanity.com/paper/2105.14774/) (2021)
- [Supervised Gradual Machine Learning for Aspect Category Detection](https://mlsanity.com/paper/2404.05245/) (2024)

## Abstract (arXiv)

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.