Ensemble application of bidirectional LSTM and GRU for aspect category detection with imbalanced data

Abstract

E-commerce websites produce a large number of online reviews, posts, and comments about a product or service. These reviews are used to assist consumers in buying or recommending a product. However, consumers are expressing their views on a specific aspect category of a product. In particular, aspect category detection is one of the subtasks of aspect-based sentiment analysis, and it classifies a given text into a set of predefined aspects. Naturally, a class imbalance problem occurs in real-world applications. The class imbalance is studied over the last two decades using machine learning algorithms. However, there is very little empirical research in deep learning with the class imbalance problem. In this paper, we propose bidirectional LSTM and GRU networks to deal with imbalance aspect categories. The proposed method applies a data-level technique to reduce class imbalance. Specifically, we employ the stratified sampling technique to deal with imbalanced classes. Moreover, we create word vectors with the corpus-specific word embeddings and pre-trained word embeddings. This word representations fed into the proposed method and their merge modes such as addition, multiplication, average, and concatenation. The performance of this method is evaluated with a confusion matrix, precision, recall, F1-score with micro-average, macro-average, and weighted average. The experimental result analysis suggests that the proposed method outperforms with pre-trained word embeddings.