# TAWC: Text Augmentation with Word Contributions for Imbalance Aspect-Based Sentiment Classification
## 1 Introduction  
Sentiment analysis is a fundamental task in natural language processing (NLP). The primary goal of the proposed model is to predict the sentiment polarity of sentences. In addition to sentiment analysis at the sentence level, different aspects of a sentence may express different sentiment polarities [1]. Aspect-based sentiment analysis (ABSA) determines fine-grained opinion polarity toward a given term. Compared to traditional text sentiment analysis, ABSA can extract more fine-grained emotional expressions, analyze users' specific emotional views of various objectives, and gain a more granular understanding of products, providing more accurate decision support to decision-makers [2]. Many algorithms, especially deep learning models, have gained popularity in ABSA due to their high learning capacity in capturing the semantics of sentences, such as recurrent neural networks (RNNs) [3], convolutional neural networks (CNNs) [4], and transformer models [5]. ABSA deep learning requires labeled datasets for training, and model performance is related to training data size; the larger the training data, the better. Adding more examples reduced the margin error and increases the model's generalizability. However, large, labeled corpora are limited because people often develop them manually, which is a time-consuming and inefficient procedure. The lack of labeled data in a low-resource corpus introduces obstacles such as model overfitting or underfitting and tends to cause the problem of class imbalance [6]. In addition, imbalance class distribution in the training data affects text classification outcomes [7]. Therefore, text data augmentation techniques are used to balance classes in terms of sample size and to improve the total sample size of small datasets. These improvements boost classification accuracy and minimize the need for manual intervention. Diverse text augmentation approaches have been proposed to expand training data, including replacing words in sentences [8], back-translation [9], and language model-based methods [10]. However, previous methods primarily augment the text randomly without considering the different contributions and roles of words to the context. Although the proposed method incurs a minimal time cost, it may lose the semantic structure and order of the original training data, resulting in changes to the enhanced semantic labels. Hence, the informed replacement of a specific word is expected to yield different results compared to the random replacement of words. Furthermore, ABSA augmentation strategies should ensure that aspect words are not substituted during augmentation because they influence the sentiment classification effect [11]. Based on our previous study using an integrated gradient attribution score [12], we found that each token in the text sequence has different contributions to the generation of the label. The text label is a word that highly generalizes the text content. However, the drawback of the proposed method is that the integrated gradient only captures positive and negative labels, whereas a sentence with neutral polarity as the minority class provides a zero score. Motivated by the above considerations, we propose a lightweight and effective augmentation method named text augmentation with word contributions (TAWC) for aspect-based sentiment tasks. The proposed method can generate diverse and clean text samples while maintaining the original meaning. It focuses on text editing techniques to improve training data, using simple modifications to the text without relying on large, pre-trained language models. The proposed method emphasizes efficiency and simplicity and provides better training samples via basic text editing operations. In particular, we first introduce word contribution identification using analytical correlation, which examines how frequently a word co-occurs with a particular label while occurring less often with other labels, and semantic similarity, which measures the semantics between a word and its polarity.