DS -Group at SIGHAN -2024 dim ABSA Task: Constructing In-context Learning Structure for Dimensional Aspect-Based Sentiment Analysis (2024)

Abstract: Aspect-Based Sentiment Analysis (ABSA) is an important subtask in Natural Language Processing (NLP). More recent research within ABSA have consistently focused on conducting more precise sentiment analysis on aspects, i.e., dimensional Aspect-Based Sentiment Analysis (dimABSA). However, previous approaches have not systematically explored the use of Large Language Models (LLMs) in dimABSA. To fill the gap, we propose a novel In-Context Learning (ICL) structure with a novel aspect-aware ICL example selection method, to enhance the performance of LLMs in dimABSA. Experiments show that our proposed ICL structure significantly improves the fine-grained sentiment analysis abilities of LLMs.

## introduction aspect-based sentiment analysis (absa) has been a significant research topic in natural language processing (nlp). the goal of absa is to identify specific aspects within a sentence and determine the corresponding sentiment polarity (positive, neutral, or negative) for each aspect @xcite . this is different from traditional sentiment analysis (sa) that provides an overall sentiment prediction for the sentence. absa has been extensively studied, resulting in numerous effective algorithms. however, human emotions are inherently continuous rather than discrete, involving two finergrained dimensions of sentiment, including valence and arousal @xcite . as illustrated in figure 1 , the valence dimension represents the degree of pleasure or displeasure sentiment, while the arousal dimension indicates the intensity of the sentiment. in this two-dimensional space, all emotions can be precisely represented. for instance, an emotion * corresponding author. with a valence of 7 and an arousal of 7 would be closer to delighted, whereas a valence of 1 and an arousal of 9 would signify a very intense negative sentiment. extending the traditional sa and absa to the two-dimensional space of sentiment has led to dimensional sentiment analysis (dsa) and dimensional aspect-based sentiment analysis (dimabsa). ## introduction sentiment analysis is an important task in opinion mining for both academic and business use. traditional sentiment analysis approaches mainly intend to identify the positive or negative sentiment polarities of text. this field has been widely researched and has many effective approaches based on rules or statistical methods. however, analyzing only the polarities of sentiments is rough and can't differ sentiment distinctions in fine-grained. in order to go further in fine-grained sentiment analysis, some approaches were proposed to address this problem in more categories or in real-value, such as dimensional sentiment analysis. evaluating sentiment in valence-arousal (va) space was first proposed by @xcite . as shown in figure 1 , the valence dimension represents the degree of positive or negative sentiment, while the arousal dimension indicates the intensity of sentiment. based on this two-dimensional representation, any affective state can be represented as a point in the va coordinate plane by determining the degrees of valence and arousal of given words @xcite @xcite or texts @xcite . external va resources like lexicons are necessary to va sentiment evaluation. however, there is a lack of these resources especially for chinese, and it's usually difficult to construct them manually. thus in order to get large scale lexicons in a reasonable cost, the objective of the shared task dsap is to automatically acquire the valencearousal ratings of chinese affective words and phrases. some typical approaches to word-level va rating task are based on statistical observations like linear regression @xcite and kernel function @xcite . however, these methods deeply rely on the affective lex-47 figure 1: valence-arousal space. the picture is originally from @xcite . as a recently emerging yet largely underinvestigated task, dimabsa aims to conduct finergrained sentiment analysis by assigning corresponding valence and arousal values to each aspect in a sentence, as illustrated in figure 2 . despite various dsa methods have been developed, which are mainly based on lexicons at the word-level or phrase-level, there is a lack of extensive and systematic studies on the aspect-level dimabsa. this paper aims to fill the gap. inspired by the success of large language models (llms) on the aspectlevel sentiment analysis tasks @xcite @xcite , we propose an in-context learning (icl) framework for dimabsa and evaluates its effectiveness on three mainstream llms: qwen-plus @xcite , gpt-3.5 (openai, 2023) and gpt-4 @xcite . the main contributions of this paper are as follows: ## methodology in this section, we introduce the task definition and the components of our proposed icl structure.