  In the context of sentiment analysis, there has been growing interest in
performing a finer granularity analysis focusing on the specific aspects of the
entities being evaluated. This is the goal of Aspect-Based Sentiment Analysis
(ABSA) which basically involves two tasks: aspect extraction and polarity
detection. The first task is responsible for discovering the aspects mentioned
in the review text and the second task assigns a sentiment orientation
(positive, negative, or neutral) to that aspect. Currently, the
state-of-the-art in ABSA consists of the application of deep learning methods
such as recurrent, convolutional and attention neural networks. The limitation
of these techniques is that they require a lot of training data and are
computationally expensive. In this paper, we propose a simple approach called
SUAEx for aspect extraction. SUAEx is unsupervised and relies solely on the
similarity of word embeddings. Experimental results on datasets from three
different domains have shown that SUAEx achieves results that can outperform
the state-of-the-art attention-based approach at a fraction of the time.
