## 1 Introduction  
Attitude bias (abbreviated as \"bias\") is people's tendentious beliefs about concepts. As an inherent motivation of people's interests and consequent behaviors, bias is essential to human-centered research, such as social psychology, computational opinion analysis, and recommendation. In current natural-language processing research, we can understand biases by analyzing the sentiment and semantics of people's public expressions. However, social psychology research reveals that people's explicitly expressed bias in public does not always agree with their implicit bias in mind [1]. People's explicit biases are conscious, controllable, and easy to report, while implicit biases are uncontrollable and cannot be consciously acquired [2]. Explicit and implicit biases play different roles in social life. Explicit bias spreads in public and forms the mainstream value, whereas implicit bias can determine the behavior without conscious awareness, e.g., voting and purchasing [3]. A well-known example is American racial bias, where self-reports reveal a near-absence of preference difference between White and Black people [4]. In contrast, implicit bias reveals a widespread preference for White people relative to Black people [5]. Implicit bias is also found to be a better predictor of certain kinds of behavior (e.g., risky flight [6], and anxiety [7]) than explicit bias. In psychology, the Self-Report Assessment (SRA) [8] and the Implicit Association Test (IAT) [5] are capable of measuring explicit and implicit attitude bias, respectively. However, both SRA and IAT need cooperation from subjects, and it is very costly for a large population. To understand the bias automatically, researchers apply natural-language processing techniques to understand bias with the semantic relations between people's words [9]. For example, [PERSON] [10] introduced that biased language in Wikipedia uncovers framing and epistemological bias and can be identified by familiar linguistic cues. Recent works show that word and sentence embedding can capture common bias in training corpora [11]. Word Embedding Association Test (WEAT) proposes to [12] measure the bias by regarding the semantic distance between pre-trained vectors of certain words as the implicit association strength in IAT. Sentence Encoder Association Test (SEAT) [13] extends the WEAT by constructing sentences containing specific words so that the word encoders (word2 vec and GloVe) in WEAT can be replaced by the sentence encoders (ELMo and BERT). Recent work [14; 15] explores the subjects of word embedding biases and finds that these biases are centered around males. Moreover, [PERSON] et al. propose hierarchical Bayesian modeling (HBM) [16] as an alternative to WEAT. HBM scrutinizes biases in word embeddings across various levels of granularity and yields robust results in bias assessments concerning gender, race, and religion. However, WEAT, SEAT, and earlier language-based approaches do not distinguish explicit and implicit biases. Without this distinguishment, if explicit and implicit biases contained in language are different, the bias identified by current methods will be a confusion of explicit and implicit biases. Against the background mentioned above, the primary research motivation of this paper is to draw inspiration from the psychological measurement of biases and employ computational techniques to address the problem of separately identifying explicit and implicit bias in a large population automatically. Based on this motivation, our research objective is to propose a novel strategy to connect the key factors in SRA and IAT with special words and statements and automatically simulate SRA and IAT with sentiment analysis of the statements. The framework is shown in Figure 1. First, we define the categories of special words named concept words, exemplar words and attribute words, corresponding to the concept, exemplar and attribute in SRA and IAT.