情感是人类情绪体验的核心概念，情绪词汇是用来描述特定情感状态的主要载体。情绪词汇心理表征包括个体心理中情绪词汇所代表的情感概念和结构， 而情绪词汇文化表现则涉及不同文化对情绪表达和理解方式的差异。心理词库 中储存的相关信息是情绪词汇心理表征的一部分，影响个体对情绪词汇的理解 和使用。由于跨语言文化之间的差异会影响词汇情感表达和理解，因此有必要 对不同语言情绪词汇差异进行研究。本研究通过对语言间和语言内部情绪词汇心理表征和文化表现的差异进行分析，系统考察情绪词汇在不同表征模态和文化间的差异性，深入探讨语言情感表达特点。本研究情绪词汇跨文化表征研究相关结果，有助于更好实现跨文化情感识别与交流。

      具体来说，研究一选取 100 名被试，通过主观评定方法在英文情感词库（Affective Norms for English Words, ANEW）基础上构建对应的汉语情感词库。选取愉悦度为主要指标，利用自然语言处理技术分别计算两种语言内部情感词库中的词汇相似度，分别构建中英文情绪词汇心理表征网络。研究二通过自然语言处理方法训练中英文维基百科语料库，结合基于词向量相似度的半监督情感极性判断算法（Sentiment orientation from word vector, SO-WV）构建中英文领域情感词典。选取情感极性维度后，使用与研究一相同的方法构建两种语言情绪词汇的文化表现网络。研究三通过复杂网络分析方法，借助复杂网络参数对上述构建的四种网络（中英文心理表征情绪词汇网络、中英文文化表现情绪词汇网络、中文心理表征和文化表现情绪词汇网络、英文心理表征和文化表现情绪词汇网络）进行对比分析。

      研究一的结果发现，汉语情感系统中两两维度的相关性和回归性均与以往研究一致，表明构建的汉语情感系统有效，可用于后续网络构建。研究二的结果发现，中英文领域情感词典中情感词的情感值分布符合正态分布，且情感词的正确率均达到 75%以上，表明 SO-WV 算法和构建的中英文领域情感词典有效，同样可用于构建后续网络。研究三的结果发现，中英文语言间和语言内部的心理表征和文化表现情绪词汇网络都表现出小世界和无标度网络的特性。不同网络参数的相关性分析发现，平均节点度可作为研究情绪词汇网络差异的主要参数。此外，研究也验证了参数度是决定情绪词汇网络其他参数的主要因素。通过分析中英文的情绪词汇网络平均节点度和参数度排名前 10 情绪词汇的差异，发现中英两种语言普遍更倾向于使用积极情绪词汇表达情感。然而，不同于中文，英语文化背景下，人们除了积极情绪的表达，还注重负面情绪的表达。通过比较中英文语言间的心理表征和文化表现的情感表达特点，研究发现中英情感在文化表现层面都更为直接，但在心理表征层面存在差异，中文情感隐晦含蓄，而英文情感简洁明确，这可能反映了中英文个体主义与集体主义的区别。此外，通过比较中英文各自语言内部心理表现和文化表现的网络特点，发现英文的内外在情感表达差异不大，而中文内外在情感表达存在差异，这可能与中文语言的结构和表达方式有关。本研究证明了基于网络观分析跨语言和跨文化情感的有效性和可行性，革新了语言学的分析思路。

    Emotion is the core concept of human emotional experience, and affective words serve as the main carrier used to describe specific emotional states. The mental representation of affective words encompasses the emotional concepts and structures represented by affective words in individual psychology, while the cultural manifestation of affective words involves the variations in emotional expression and understanding across different cultures. As an important aspect of the affective word mental representation, the relevant information stored in the mental lexicon has an impact on individuals' comprehension and usage of such words. Considering the impact of cross-linguistic and cross-cultural differences on emotional word expression and comprehension, it is essential to conduct research on affective language differences. This research systematically examined the differences in mental representation and cultural manifestation of affective words within and across languages. The investigation of the variability of affective words across different representational modalities and cultures can provide an in-depth exploration of the linguistic characteristics of emotional expression. The findings concerning the cross-cultural representation of affective words can, to some extent, enhance cross-cultural emotional recognition and communication.

    Specifically, 100 participants in research one were recruited to construct a Chinese Affective System using subjective rating methods based on the Affective Norms for English Words (ANEW). Valence was utilized as the primary index, and mental representations of affective words were constructed in each language through natural language processing techniques that calculated lexical similarity. In research two, natural language processing techniques were utilized to train Chinese and English Wikipedia corpora. Subsequently, a domain sentiment lexicon was constructed for both languages using the sentiment orientation from the word vector (SO-WV) algorithm. After selecting an emotional polarity dimension, two cultural manifestation networks of affective words in different languages were constructed using the same methodology as in the first research. Research three performed a comparative analysis on the four networks constructed in the previous studies, namely the Chinese and English affective word networks in the mental representation, the Chinese and English affective word networks in the cultural manifestation, the Chinese affective word networks in the mental representation and cultural manifestation, and the English affective word networks in the mental representation and cultural manifestation.

    The results of research one revealed that the correlation and regression of any two dimensions in the Chinese Affective System were consistent with previous research, thereby indicating the validity of the constructed Chinese Affective System for subsequent network construction. The results of research two demonstrated that the emotional values of affective words in both Chinese and English domain sentiment lexicons followed a normal distribution. Additionally, the accuracy of affective word classification exceeded 75%. These results indicated that the SO-WV algorithm and the constructed Chinese and English domain sentiment lexicons were effective, and can be used for subsequent network construction. The results of research three showed that the affective word networks within and between languages exhibited characteristics of small-world and scale-free networks. Moreover, the study identified that the average node degree was the primary parameter for exploring differences in affective word networks. Additionally, through further analysis of the differences in average node degree and the top 10 affective words ranked by degree in affective word networks within and between languages, the study found that both Chinese and English tend to use positive affective words to express emotions. However, unlike in Chinese culture, in English, people not only emphasize the expression of positive emotions but also pay attention to expressing negative emotions. By comparing the emotional expression characteristics of mental representation and cultural manifestation in Chinese and English languages, the study observed that both cultures tend to display more direct emotional expressions in their external cultural manifestations. Nonetheless, differences existed in the internal mental lexicon of the two languages, with Chinese being more implicit and English being more concise and clearer. The contrast may reflect the difference between individualism and collectivism in Chinese and English cultures. Moreover, comparing the internal mental and cultural manifestation of Chinese and English languages revealed that while there was no significant difference in internal and external emotional expression in English, a difference was observed in Chinese. The distinction may be linked to the structure and expression patterns of the Chinese language. The current study demonstrated the effectiveness and feasibility of analyzing cross-lingual and cross-cultural emotions through network observation, thus innovating the analytical approach of linguistics.

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