# TAWC: Text Augmentation with Word Contributions for Imbalance Aspect-Based Sentiment Classification
## 1 Introduction
Words with varying degrees of correlation and semantic similarity should contribute differently to the sentiment classification task. The sentence's words can be categorized into four groups according to the two perspectives (high and low degrees, as illustrated in Figure 1. Take the following example of a sentence with negative sentiment: _If you do not mind pre-sliced low quality fish unfriendly staff and a chef that looks like he is miserable then this is your staff_. The words _low_, _unfriendly_, and _miserable_ are identified as words with high correlation and high similarity to the negative label. In contrast, _mind_ is categorized in the irrelevant group with low correlation and similarity. Furthermore, _pre-sliced_ and _looks_ are included in the necessity words category, while the low correlation and high similarity categories consist of the words _quality_ and _like_. Subsequently, we selectively replace words in the sentence according to their groups during augmentation. We used word embedding models rather than WordNet to identify the most similar words for synonym replacement. Word embedding is one of the most popular representations of document vocabulary, and it can capture the context of a word in a document, its semantic and syntactic similarity, and its relationships with other words [13]. We design appropriate methods to handle the challenges of low-resource aspect-based fine-grained sentiment classifications. The proposed strategies balance the training data by oversampling minority classes and increasing the data size to improve the learning efficiency and accuracy of the aspect-based text sentiment classifier. Moreover, they enhance the portability and interpretability of the classification model on the benchmark ABSA datasets, namely SemEval 2015 and SemEval 2016, for the restaurant domain. We consider three representative pre-trained language models: the BERT [5], DistilBERT [14], and RoBERTa [15] classification models. The experimental results demonstrate that the proposed selective word augmentation outperforms the baseline methods. The contributions are summarized as follows:  
1. We introduce an effective method to determine individual word contributions in a sentence. Word contributions represent the relationship between words and sentiment polarity considering both analytical and semantic perspectives, which guarantee the recognition accuracy of the word contributions. 2. We propose a selective augmentation method based on word contributions that uses synonyms by measuring the similarity between word embeddings. The proposed method generates a relatively clean sample and is simple to implement. 3. We conduct extensive comparative experiments to verify the effectiveness of the proposed augmentation approaches. 4. We present a lightweight word extraction strategy that can motivate research in other disciplines such as information retrieval and document representation. The remainder of this paper is organized as follows: Section 2 introduces related works on data augmentation methods. Section 3 presents the proposed method. Section 4 describes the experimental setup. Section 5 demonstrates the comparative experiment and discussion. Section 6 concludes the paper and discusses future work.