基于情感词典的创新情感分析方法在捕捉情感信息方面显示出显著优势，例如个体态度、经验和需求，为乳腺癌（BC）患者的情感识别和管理提供了新的视角和方法。然而，目前，在BC领域的情感分析受到限制，并且没有专门用于该领域的情感词典。因此，有必要构建一个符合BC患者特征的情感词典，以为准确识别和分析患者情感提供新工具，为他们的个性化情感管理提供新方法。本研究旨在构建BC患者情感词典。通过合并两个通用情感词典（“Chinese Linguistic Inquiry and Word Count”和HowNet）中的词汇以及通过微博、半结构化访谈和表达式写作获取的来自BC患者的文本语料库中的词汇，获得了情感词。在Russell的价值-唤起空间指导下，通过手动注释和分类构建了词典。我们将Ekman的基本情感类别、Lazarus的情感认知评估理论以及基于BC患者文本语料库的定性文本分析相结合，确定了构建的词典的细粒度情感类别。采用精准率、召回率和F1得分评估词典的性能。从不同阶段的BC患者收集的文本语料包括150份书面材料、17个访谈和6689个微博原始帖子和评论，共计1923593个汉字。BC患者的情感词典包含了9357个词汇，并涵盖了8个细粒度情感类别：喜悦、愤怒、悲伤、恐惧、厌恶、惊讶、躯体症状和BC术语。实验结果表明，积极情感词的精确率、召回率和F1得分分别为98.42％、99.73％和99.07％，消极情感词的精确率、召回率和F1得分分别为99.73％、98.38％和99.05％，均明显优于C-LIWC和HowNet。具有细粒度情感类别的情感词典符合BC患者的特征。与C-LIWC和HowNet相比，它在识别和分类BC特定情感词方面的表现更好。该词典不仅为BC领域的情感分析提供了新工具，还为认识BC患者的特定情感状态和需求以及制定个性化情感管理计划提供了新的视角。© Chaixiu Li、Jiaqi Fu、Jie Lai、Lijun Sun、Chunlan Zhou、Wenji Li、Biao Jian、Shisi Deng、Yujie Zhang、Zihan Guo、Yusheng Liu、Yanni Zhou、Shihui Xie、Mingyue Hou、Ru Wang、Qinjie Chen、Yanni Wu。原始发表于《医学互联网研究杂志》（https://www.jmir.org），2023年9月12日发布。

The innovative method of sentiment analysis based on an emotional lexicon shows prominent advantages in capturing emotional information, such as individual attitudes, experiences, and needs, which provides a new perspective and method for emotion recognition and management for patients with breast cancer (BC). However, at present, sentiment analysis in the field of BC is limited, and there is no emotional lexicon for this field. Therefore, it is necessary to construct an emotional lexicon that conforms to the characteristics of patients with BC so as to provide a new tool for accurate identification and analysis of the patients' emotions and a new method for their personalized emotion management.This study aimed to construct an emotional lexicon of patients with BC.Emotional words were obtained by merging the words in 2 general sentiment lexicons, the Chinese Linguistic Inquiry and Word Count (C-LIWC) and HowNet, and the words in text corpora acquired from patients with BC via Weibo, semistructured interviews, and expressive writing. The lexicon was constructed using manual annotation and classification under the guidance of Russell's valence-arousal space. Ekman's basic emotional categories, Lazarus' cognitive appraisal theory of emotion, and a qualitative text analysis based on the text corpora of patients with BC were combined to determine the fine-grained emotional categories of the lexicon we constructed. Precision, recall, and the F1-score were used to evaluate the lexicon's performance.The text corpora collected from patients in different stages of BC included 150 written materials, 17 interviews, and 6689 original posts and comments from Weibo, with a total of 1,923,593 Chinese characters. The emotional lexicon of patients with BC contained 9357 words and covered 8 fine-grained emotional categories: joy, anger, sadness, fear, disgust, surprise, somatic symptoms, and BC terminology. Experimental results showed that precision, recall, and the F1-score of positive emotional words were 98.42%, 99.73%, and 99.07%, respectively, and those of negative emotional words were 99.73%, 98.38%, and 99.05%, respectively, which all significantly outperformed the C-LIWC and HowNet.The emotional lexicon with fine-grained emotional categories conforms to the characteristics of patients with BC. Its performance related to identifying and classifying domain-specific emotional words in BC is better compared to the C-LIWC and HowNet. This lexicon not only provides a new tool for sentiment analysis in the field of BC but also provides a new perspective for recognizing the specific emotional state and needs of patients with BC and formulating tailored emotional management plans.©Chaixiu Li, Jiaqi Fu, Jie Lai, Lijun Sun, Chunlan Zhou, Wenji Li, Biao Jian, Shisi Deng, Yujie Zhang, Zihan Guo, Yusheng Liu, Yanni Zhou, Shihui Xie, Mingyue Hou, Ru Wang, Qinjie Chen, Yanni Wu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.09.2023.