Semi-Supervised Learning Based on Auto-generated Lexicon Using XAI in Sentiment Analysis (2021)

Abstract: In this study, we proposed a novel Lexicon-based pseudo-labeling method utilizing explainable AI(XAI) approach. Existing approach have a fundamental limitation in their robustness because poor classifier leads to inaccurate soft-labeling, and it lead to poor classifier repetitively. Meanwhile, we generate the lexicon consists of sentiment word based on the explainability score. Then we calculate the confidence of unlabeled data with lexicon and add them into labeled dataset for the robust pseudo-labeling approach. Our proposed method has three contributions. First, the proposed methodology automatically generates a lexicon based on XAI and performs independent pseudo-labeling, thereby guaranteeing higher performance and robustness compared to the existing one. Second, since lexicon-based pseudo-labeling is performed without re-learning in most of models, time efficiency is considerably increased, and third, the generated high-quality lexicon can be available for sentiment analysis of data from similar domains. The effectiveness and efficiency of our proposed method were verified through quantitative comparison with the existing pseudo-labeling method and qualitative review of the generated lexicon.

## introduction sentiment analysis is employed to identify the sentiment orientation and measure the emotional strength @xcite @xcite . to better understand information generated by online user and to take advantage of it, sentiment analysis is becoming major topic in text mining field in last two decades @xcite @xcite . previous studies of sentiment analysis can be roughly categorized into two different groups: 1) lexicon-based approaches and 2) machine learning-based approaches @xcite . the lexicon-based approaches efficiently calculate the sentiment score of sentence of document since it does not need to train the classification model in advance. however, the lexicon-based approaches depends on the availability of a sentiment lexicon which is collection of manually pre-created sentiment words lexicon and its sentiment polarity @xcite @xcite . meanwhile, the machine-learning based approaches require a training set consists of labeled data (e.g. positive, negative or neutral). to address the challenge caused by limited labeled data, which is the usual case in practice, semi-supervised learning have attracted more attention recently @xcite @xcite . the semi-supervised learning can be divided into several categories such as consistency regularization approaches, entropy minimization approaches and augmentation based approaches and pseudo-labeling approaches. among those approaches, the pseudo-labeling approaches are one of the most intuitive and widely used semi-supervised learning in sentiment analysis @xcite @xcite . the pseudo-labeling approaches tried to train the sentiment classification model with small number of labeled data and add unlabeled data with high-confidence of sentiment score, calculated by ## method as aforementioned, instead of existing approaches which calculate the confidence of sentiment score for unlabeled dataset by same classifier, we calculated the confidence of sentiment score for unlabeled dataset by autogenerated lexicon. that is, a lexicon is automatically generated through the explainability score calculated while learning the classifier with labeled data, and pseudo-labels are assigned to the unlabeled dataset based on the generated lexicon. the summary of our proposed method is illustrated in figure 2 . in detail, the learning process in the work of creating lexicon is as follows. first, a binary classification model is trained using data with positive and negative la bels. and the importance score of each word is grasped through the coefficients derived from each model. an initial lexicon is created based on this importance score. second, pseudo-labels are additionally assigned to n unlabeled data using the generated lexicon, and the previous process is repeated using n additional data, and the lexicon is updated based on this result. when creating a lexicon, each word's importance score is assigned as the average of the word's scores each time the dictionary is updated. by repeating the process, the lexicon is updated and the process of assigning pseudo-labels to unlabeled data is completed. these processes are defined formally in the algorithm 1. algorithm 1 creating sentiment lexicon 1: obtain a small set of 𝐿 of labeled examples 2: obtain a large set of 𝑈 of unlabeled examples 3: for 𝑁 iterations do 4: for each explainable classifier 𝐶 𝑖 do 5: learn classifier 𝐶 𝑖 from 𝐿 6: end for 7: update lexicon 𝐷 from ensemble of 𝐶 𝑖 8: choose confidently predicted example 𝐸 from 𝑈 based on normalized 𝐷 9: 𝐸 is removed from 𝑈 and added (with their given labels) to 𝐿 10: end for ## conclusion in this study, a novel lexicon-based pseudolabeling method utilizing xai approach was proposed that improved the limitations of the existing pseudo-labeling method. the existing approaches have a fundamental limitation in their robustness because soft labeling task and classification task completely depends on each other. however, the proposed methodology automatically generates a lexicon based on xai and performs independent pseudo-labeling, thereby guaranteeing higher performance and robustness compared to the existing one. in addition to robustness, since dictionary-based pseudo-labeling is performed without re-learning, time efficiency is considerably increased, the generated high-quality lexicon can be available for sentiment analysis of data from similar domains. the quantitative excellence of the proposed method was verified through a one-to-one performance comparison with the existing method, and the effectiveness and efficiency of the proposed method were qualitatively verified by reviewing the generated lexicon. future research may extend the scope of xai based lexicon construction in a more general point of view. as shown in the experimental results, there are differences in lexicons for each domain, and a study to construct a general-domain lexicon by integrating them is presented as a future work. moreover, such studies can be expected to aid in the widespread application of the proposed semisupervised learning in various tasks arising within the natural language processing domain.