Tsetlin Machine for Sentiment Analysis and Spam Review Detection in Chinese

Abstract

In Natural Language Processing (NLP), deep-learning neural networks have superior performance but pose transparency and explainability barriers, due to their black box nature, and, thus, there is lack of trustworthiness. On the other hand, classical machine learning techniques are intuitive and easy to understand but often cannot perform satisfactorily. Fortunately, many research studies have recently indicated that the newly introduced model, Tsetlin Machine (TM), has reliable performance and, at the same time, enjoys human-level interpretability by nature, which is a promising approach to trade off effectiveness and interpretability. However, nearly all of the related works so far have concentrated on the English language, while research on other languages is relatively scarce. So, we propose a novel method, based on the TM model, in which the learning process is transparent and easily-understandable for Chinese NLP tasks. Our model can learn semantic information in the Chinese language by clauses. For evaluation, we conducted experiments in two domains, namely sentiment analysis and spam review detection. The experimental results showed thatm for both domains, our method could provide higher accuracy and a higher F1 score than complex, but non-transparent, deep-learning models, such as BERT and ERINE.

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“…Tang et al [29] discussed three data enhancement techniques, including synonym substitution, random insertion, and random swapping, that can improve the robustness of pretrained models, such as BERT and DistilBERT, against adversarial attacks in text categorization tasks. Zhang et al [30] proposed a new approach based on the testkin machine model for Chinese natural language processing tasks. The learning process of this method is transparent and easy to understand compared to deep-learning-based models.…”