Exploring Logographic Image for C hinese Aspect-based Sentiment Classification (2022)

Abstract: In logographic languages like Chinese, word meanings are constructed using specific character formations, which can help to disambiguate word senses and are beneficial for sentiment classification. However, such knowledge is rarely explored in previous sentiment analysis methods. In this paper, we focus on exploring the logographic information for aspect-based sentiment classification in Chinese text. Specifically, we employ a logographic image to capture an internal morphological structure from the character sequence. The logographic image is also used to learn the external relations among context and aspect words. Furthermore, we propose a multimodal language model to explicitly incorporate a logographic image with review text for aspect-based sentiment classification in Chinese. Experimental results show that our method brings substantial performance improvement over strong baselines. The results also indicate that the logographic image is very important for exploring the internal structure and external relations from the character sequence.

## introduction aspect-based sentiment classification aims to determine the polarity of the aspect in a sentence. it can provide a fine-grained analysis of the users' opinions towards the specific aspect. consequently, it has aroused much research attention in recent years. pre-training methods @xcite and graph-based models with dependency relations @xcite have achieved great success in this task. while previous work has predominantly focused on english settings @xcite @xcite , recent work has also studied on chinese text. for example, @xcite proposed a topic-based model to discover correlations between topic and aspects in short chinese sentences. @xcite al. (2021 a sequence-to-sequence model to generate opinion target-word pairs from chinese review text. however, chinese is a language derived from pictographs and essentially different from english or other phonetic languages @xcite . that means not only words and characters can express specific meanings, but also radicals are important carriers of semantics. for example, the characters with "木" (wood) radical, e.g., "桌" (desk), "椅" (chair), tend to be aspect terms in the furniture domain. also, the characters with "心" (heart) radical, e.g, "惠" (cheap), "惊" (surprise) tend to express opinion toward an aspect term. in addition, it is hard to recognize the explicit word boundaries and capture the relation between opinion words and aspect terms in chinese sentences. as shown in figure 1 , the meaning of "美 味" (delicious) cannot be derived by "美" (beautiful) and "味" (taste). also, the model should build a connection accurately between the opinion word "美味" and the aspect term "甜品" @xcite . in this study, we employ logographic image to address the above challenges. as shown in figure 1 , we firstly convert the character sequence into a logographic image to capture an internal morphological structure. we then employ an aspect-oriented dependency tree to represent the external structure among context and aspect words, and convert it into the logographic image. the advantage is that the logographic image pro-vides a natural way to represent the internal morphological structure of characters. in addition, it is easy to encode the external structure using the logographic image than using graph nerual networks. to this end, we propose a multimodal language model to explicitly incorporate a logographic image into chinese aspect-based sentiment classification model. given a review sentence, we first employ a text encoder to learn the text representation, we then utilize an image encoder to learn the visual representation from the logographic image. afterward, we propose a multimodal interaction model to predict the polarity from text and vision modality jointly. the experimental results show that our model outperforms competitive models. in addition, the results also indicate the effect of the proposed logographic image with internal structure and external relations. in summary, the contributions of this paper include: ## related work in this section, we give some related works on aspect-based sentiment classification and nlp applications using logographic information. ## logographic image construction in this section, we illustrate the construction process of the logographic image. the process can be separated into two stages: sequence image construction which aims to capture the internal morphological structure of characters, and converts the character sequence to the image; structural image construction which aims to encode the external relations among aspect terms and context to image with aspect-oriented dependency tree. ## multimodal language model in this study, we employ a multimodal language model to integrate logographic images for aspectbased sentiment classification. figure 4 gives an overview of the proposed model. given a review sentence, we employ the text encoder to learn the text representation and utilize the image encoder to learn the visual representation from the logographic images with different typefaces. then, we propose a multimodal interaction model to learn multimodal hidden representation from text and vision modality jointly. afterward, we predict the polarity based on the multimodal hidden representation. in the below of this section, we will discuss these issues one by one. ## experimentation in this section, we introduce the datasets used for evaluation and the baseline methods employed for comparison. we then report the experimental results conducted from different perspectives and analyze the effectiveness of the proposed model with different factors. ## analysis and discussion in this section, we give some analysis and discussion to show the effectiveness of the logographic image with internal structure and external relation. ## conclusion in this study, we focus on exploring the logographic information for aspect-based sentiment classification in chinese text. in particular, we employ a logographic image to capture an internal morphological structure from the character sequence. the logographic image is also used to learn the external relations among context and aspect words. furthermore, we propose a multimodal language model to explicitly incorporate a logographic image with review text into chinese aspect-based sentiment classification. the experimental results indicate the effectiveness of the proposed model. these also justify the importance of the logographic image with internal structure and external relations information.