Article(id=1322165025628865361, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322164984558243894, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.01.024, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740240000000, receivedDateStr=2025-02-23, revisedDate=1763481600000, revisedDateStr=2025-11-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1793158306747, onlineDateStr=2026-10-28, pubDate=1767974400000, pubDateStr=2026-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793158306747, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793158306747, creator=13701087609, updateTime=1793158306747, updator=13701087609, issue=Issue{id=1322164984558243894, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='1', pageStart='1', pageEnd='176', issueExtLink='null', onlineDate='null', pubDate='1767974400000', pubDateStr='2026-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793158296955, creator='13701087609', updateTime=1793158296955, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=160, endPage=164, ext={EN=ArticleExt(id=1322165025830191954, articleId=1322165025628865361, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Application progress of sentiment analysis in the nursing field, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=

This article systematically reviewed the relevant concepts and technical principles of sentiment analysis.It summarized its specific application scenarios in the nursing field from three dimensions:clinical nursing practice,nursing management optimization,and nursing education innovation.The challenges faced during the implementation of this technology were discussed,and targeted solutions were proposed,aiming to provide theoretical reference and practical guidance for the intelligent development of the nursing field.

, authors=Fei SUN, Li YUAN, Shuixian ZHANG, Xia LEI, Aiping FU, Lin ZHOU, authorsList=Fei SUN, Li YUAN, Shuixian ZHANG, Xia LEI, Aiping FU, Lin ZHOU, authorCompany=null, correspAuthors=Lin ZHOU, authorNote=null, correspAuthorsNote=
ZHOU Lin, E-mail:
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系统梳理情感分析的相关概念与技术原理,从临床护理实践、护理管理优化及护理教育革新3个维度综述其在护理领域的具体应用场景,探讨技术实施过程中面临的挑战,并提出针对性解决策略,以期为护理领域智能化发展提供理论参考与实践指导。

, authors=孙菲, 袁莉, 张水仙, 雷霞, 傅爱萍, 周临*, *, authorsList=孙菲, 袁莉, 张水仙, 雷霞, 傅爱萍, 周临, authorCompany=null, correspAuthors=周临, authorNote=

孙菲,护师,硕士研究生

, correspAuthorsNote=
周临,E-mail:
ZHOU Lin, E-mail:
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孙菲,护师,硕士研究生

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孙菲,护师,硕士研究生

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情感分析在护理领域中的应用进展
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孙菲 , 袁莉 , 张水仙 , 雷霞 , 傅爱萍 , 周临 *, *
护理研究 | 科研综述 2026,40(1): 160-164
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护理研究 |科研综述 2026 , 40 (1) : 160 -164
情感分析在护理领域中的应用进展
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孙菲, 袁莉, 张水仙, 雷霞, 傅爱萍, 周临*, *
作者信息
  • 1西湖大学医学院附属杭州市第一人民医院,浙江 310006
通讯作者:
周临,E-mail:
ZHOU Lin, E-mail:
作者简介:

孙菲,护师,硕士研究生

Application progress of sentiment analysis in the nursing field
Fei SUN, Li YUAN, Shuixian ZHANG, Xia LEI, Aiping FU, Lin ZHOU
Affiliations
  • 1The Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Zhejiang 310006 China
  • Corresponding Author:
    ZHOU Lin, E-mail:
出版时间: 2026-01-10 doi: 10.12102/j.issn.1009-6493.2026.01.024
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系统梳理情感分析的相关概念与技术原理,从临床护理实践、护理管理优化及护理教育革新3个维度综述其在护理领域的具体应用场景,探讨技术实施过程中面临的挑战,并提出针对性解决策略,以期为护理领域智能化发展提供理论参考与实践指导。

情感分析  /  自然语言处理  /  临床护理  /  护理管理  /  护理教育  /  综述

This article systematically reviewed the relevant concepts and technical principles of sentiment analysis.It summarized its specific application scenarios in the nursing field from three dimensions:clinical nursing practice,nursing management optimization,and nursing education innovation.The challenges faced during the implementation of this technology were discussed,and targeted solutions were proposed,aiming to provide theoretical reference and practical guidance for the intelligent development of the nursing field.

sentiment analysis  /  natural language processing  /  clinical nursing  /  nursing management  /  nursing education  /  review
孙菲, 袁莉, 张水仙, 雷霞, 傅爱萍, 周临. 情感分析在护理领域中的应用进展. 护理研究, 2026 , 40 (1) : 160 -164 . DOI: 10.12102/j.issn.1009-6493.2026.01.024
Fei SUN, Li YUAN, Shuixian ZHANG, Xia LEI, Aiping FU, Lin ZHOU. Application progress of sentiment analysis in the nursing field[J]. Chinese Nursing Research, 2026 , 40 (1) : 160 -164 . DOI: 10.12102/j.issn.1009-6493.2026.01.024
随着数字医疗时代的到来,病人情绪状态的精准识别已成为提升护理质量的关键环节。情感分析(sentiment analysis,SA)作为自然语言处理技术的核心分支,通过解析文本中的情感倾向与主观信息,为理解病人心理需求提供了工具[1]。该技术最初应用于社交媒体舆情监测,通过挖掘用户评论中的情感特征,实现消费行为预测与市场趋势分析[2]。近年来,情感分析在医疗领域的价值逐渐显现,研究表明对电子病历叙事记录、在线健康社区发帖及可穿戴设备文本数据进行情感解析,可有效识别病人焦虑和抑郁等心理状态,较传统量表评估效率提升[3-4]。特别是在护理实践场景中,情感分析技术也展现出独特优势,研究证实通过解析术后病人疼痛描述的情感特征,护理团队可动态优化镇痛策略[5];通过对老年慢性病病人社交媒体文本的语义分析,能提前预警治疗依从性下降[6]。基于此,现对情感分析的相关概念、技术原理、临床护理实践、护理管理优化及护理教育进行综述,指出其在护理应用过程中面临的挑战,并提出面向护理实践的解决方案,以期为构建智能化和精准化的护理服务体系提供理论依据。
情感分析是自然语言处理的一个重要子领域,是通过计算模型识别、提取和量化文本、语音等非结构化数据中情感倾向的技术,其核心在于将人类主观情感转化为可分析的客观指标[7]。技术演进历经3个阶段,在规则驱动阶段主要是依赖人工构建情感词典及规则匹配实现情感判断;在统计学习阶段主要是融合传统机器学习与特征工程,通过词法-句法特征融合完成向数据驱动范式转型;在深度表征阶段主要是基于变换器的双向编码器表征(bidirectional encoder representations from transformers,BERT)等预训练语言模型构建端到端框架,通过自注意力机制实现上下文感知建模,其性能随算力提升与多模态融合持续突破[1]。目前,情感分析已经能够从大量文本数据中提取有价值的情感信息,并被广泛应用于多个领域。在市场营销中,企业利用情感分析检测社交数据中的情感倾向,以衡量品牌声誉和优化客户体验[8];在客户服务中,它帮助识别客户反馈中的关键问题,以提升服务质量[2];在临床服务中,情感分析通过解析病人反馈和社交媒体内容,帮助医护人员更好地理解病人的情绪状态和心理需求,有助于实现早期干预和有效管理[9]。随着深度学习方法不断进步,情感分析应用范围持续扩展,准确性逐步提升,成为推动各行业精准决策和服务优化的重要工具。
情感词典作为自然语言处理的核心工具,是由标注情感极性(积极/消极/中性)及强度值的词汇库构成的计算语言学资源,其通过量化词汇情感倾向(如“疼痛”标注为消极/强度0.8)为文本情感解析提供基准[10]。在临床应用中,该工具可系统解码病人自由文本中的情绪信号,为服务改进提供量化依据[11]。然而,通用情感词典面临临床适配瓶颈,存在疾病特异性表述缺失(如孕妇“发紧发硬”隐喻子宫收缩)及语境依赖性情感偏差(部分病人的“麻木”多指生理症状而非心理体验)[12]。目前,构建专病情感词典已成迫切需求,研究表明专用词典通过纳入地域性表述后,有助于提升心理问题识别覆盖率[13]。因此,建议采用“医学术语嵌入+病人语料学习”的构建路径,整合国际疾病分类(international classification of diseases,ICD)编码体系与社区文本特征,建立疾病导向的情感分析标准,以促进精准医疗实践发展。
情感分析在临床研究中的实施遵循标准化操作流程[1,14]。第1步:研究者需要收集与预处理数据,一般是从社交媒体、评论、调查问卷及电子健康记录等多源收集文本数据,对其进行清理(去除无关字符、转换为小写、去除停用词)和标记化处理,以确保数据的准确性和可用性。第2步:提取特征,通过词袋模型、词频-逆文档频率或词嵌入技术,将文本转化为可供机器学习算法使用的特征表示形式,以捕捉词语间的语义关系。第3步:情感标注与训练数据准备,对一部分文本数据进行情感标注,准备训练所需的数据集,并合理划分成训练集、验证集和测试集,以确保模型的有效性和泛化能力。第4步:模型选择与训练,选择合适的模型,并使用标注好的数据进行训练,优化参数以提高分类准确性。第5步:模型评估与优化,根据评估指标评估模型的性能,进行交叉验证和超参数调优。第6步:应用与部署,模型应用于实际未标注的文本数据上,持续监控其表现并定期更新模型。第7步:结果解释与报告,对分析结果进行解释并撰写详细的报告,确保结果易于理解和行动。
情感分析方法体系可根据分析维度与技术路径进行多层级划分,按情感解析深度可分为极性分类(二分类/三分类/多级强度)、细粒度分析(针对特定症状或治疗环节的情感倾向识别)及情绪类型判别(如区分焦虑、抑郁等6种心理状态)[15-16]。按技术实现方式涵盖基于规则的方法(依赖人工制定语法模式)、机器学习方法(如支持向量机结合词频-逆文档频率特征)及深度学习方法[BERT等预训练模型],其中深度学习方法在复杂临床文本处理中展现优势[17]。理解这些分类方式有助于临床工作者根据实际临床问题需要选择最合适的情感分析策略,比如临床应用场景适配需重点考量数据特性,社交媒体分析侧重时效性与噪声过滤;电子病历解析则需整合医学术语库[4]。研究显示,在慢性病管理场景中,结合症状本体库的细粒度分析可使用药依从性预测准确率提升[18],而在术后疼痛评估中采用双向长短期记忆网络-条件随机场架构的情绪分类模型敏感度较好[19]。因此,临床工作者应根据目标需求选择适配方法,同时关注多模态融合等前沿技术发展,以提升情感分析在真实医疗场景中的实践价值。
对在线健康社区病人进行情感分析,可辅助识别病人的情绪波动,且有助于发现服务中的优点与不足,对改进医疗服务质量和提升病人满意度、预测和改善病人依从性以及评估与改善病人心理状态至关重要。在慢性病领域,Dai等[18]应用潜在狄利克雷分配模型结合情感分析方法,对慢性病病人15 682条医疗评论进行文本挖掘,揭示病人核心服务需求,提出通过优化住院流程及挂号系统提升满意度的管理建议。在癌症领域,Rao等[20]采用自然语言处理与情感分析技术,对733条喉癌推文进行情感分类,并提取治疗、风险因素、症状诊断、预防及情感影响等主题,为临床治疗与病人自我管理提供理论依据。也有学者采用深度学习技术分析在线癌症社区情感动态,揭示其对病人需求满足的促进作用[21]。同时,Li等[22]开发乳腺癌病人情感词典,为护理决策提供工具支持;Masiero等[23]基于该工具解析病人口服治疗依从性的内外部因素,辅助医护人员识别情绪-行为关联机制,通过多维度分析优化干预策略,以提升生存率及生活质量。此外,情感分析在妇产科应用呈现多维度进展,Talbot等[24]通过社交媒体文本挖掘发现孕产妇焦虑、抑郁等心理问题存在临床识别不足的问题,建议临床关注孕产妇社交媒体心理健康监测;Chivers等[25]基于网络论坛数据分析,揭示初产妇群体过度聚焦婴儿健康而忽视自身身心状态,提示需强化关注产褥期母亲健康;我国学者采用情感分析人们对“三孩政策”的看法,量化“三孩政策”舆论情感倾向,发现持消极及中立态度者占比高于“二孩政策”,其主要原因涉及经济负担加重、女性职业困境及婚育观念变迁等,建议政府制定减负政策并引导婚育观念转型以提升生育意愿[26]。
护理管理过程往往将情感分析应用于护理人员职业压力与倦怠监测、病人满意度深度解析与服务质量改进、团队协作效能评估与冲突预警以及资源分配与排班优化中[27]。有研究显示,通过定期的情感分析,管理层可以评估护理人员的情绪状态和压力水平,识别出可能面临职业倦怠或心理困扰的员工,这有助于早期干预和支持,防止长期的压力累积导致出现更严重的心理健康问题[28]。Jura等[29]使用情感分析和聚类分析评估注册护士的工作满意度,结果显示,对工作量、办公辅助支持、护士人数及同事技能感到满意的员工通常会有更高的工作满意度;此外,是否有机会参与到管理决策中、是否得到应有的认可以及是否有足够的职业发展机会等因素共同影响了员工的整体工作满意度;然而,提升护理人员的工作满意度和个人发展也能间接改善病人的治疗结局和服务体验。同时,借助情感分析工具能够更好地发现团队内部潜在的沟通障碍或冲突点,从而采取措施改善团队间的合作氛围,促进更加有效的信息共享和协调[30]。目前,有学者结合情感分析可以实现更加科学、合理的护理排班,有助于降低人力资源成本,同时推动管理效率和服务质量的双重提升[31]。综上所述,情感分析为护理管理提供了从“经验驱动”到“数据驱动”的转型路径。通过精准识别情绪信号,管理者可针对性优化人力资源配置、服务质量及团队协作模式。未来需进一步开发临床专用工具,以实现技术应用的可持续发展。
情感分析技术正逐步成为提升教育质量的关键工具,在护理教育中往往将情感分析用于管理在线学习社区、实时监测学生情绪、精准评估临床能力及优化教学方法。随着教育数字化转型加速,学习者愈发依赖数字化学习平台获取知识,这使得学习社区的情感氛围管理成为提升在线教育质量的关键维度。有研究通过构建基于深度学习的情绪动态监测模型,实现了对讨论区文本情感极性的实时识别、群体情绪波动的可视化呈现,以及负面情绪聚集的早期预警,为构建积极学习生态提供了可量化的技术路径,也为高等院校在线教育提供了依据[32]。文字是人类传达感情、表达思想的基本信息,也是个体心理状态的重要外在表现,挖掘护理学生在发布内容时的心理状态和情绪态度对准确识别心理健康状况也起着重要作用,特别是实习护生在临床实践过程中会面临考试压力和存在临床实习焦虑[33],对在线学习平台讨论帖、课程评价问卷、学生日记等非结构化文本进行情感分析,根据情感极性分类进行实时推送减压资源,可调试学生的压力水平[34]。也有教育者据此设计虚拟现实模拟训练以及游戏教学,在模拟临床场景中,通过情感分析评估学生的沟通技巧与同理心水平[35]。而黄迪等[36]应用情感分析探究了护理本科生在玩教育游戏时产生的积极情感及其影响因素,结果发现,他们在游戏中体验到的积极情感主要与沉浸感、竞争性和评价设计有关;而成就感、真实性、趣味性和教育性也是重要的诱导因素;提示医务工作者在设计护理类教育游戏时,应注重提升沉浸体验、合理安排竞争元素和优化评价机制,以增强学习效果。
网络热点话题与真实的社会情绪息息相关,网络社区实时推送的话题,以及网络社区用户的无限评论、转发、点赞,使得舆论传播的速度较快[37]。因此,在网络社区话题的背景下,网络社区用户容易出现群体两极分化甚至导致互联网上或现实生活中的群体事件。在线用户表达的评论情绪不仅会影响整个话题的舆论传播,还会受到其他用户的影响。Bui等[38]基于因果分析发现社区回复情绪影响发起者情绪倾向,证实正向互动可提升情绪,反之则加剧负面情绪,揭示了在线社交情感传递机制,建议通过优化互动功能、规范言论及主动干预等策略促进正向情感交互,以增强癌症幸存者的社会支持。尤其在妇产领域,网络情绪更加容易感染与扩散,Teoh等[39]通过情感分析发现宫颈癌相关推文中人乳头瘤病毒疫苗及筛查态度总体积极,但低接种率地区负面舆情高发,多数人认为接种疫苗可能会增加发生卵巢衰竭等健康风险,需加强不实信息溯源治理以优化公众健康决策。万忆等[40]基于知乎数据解析“恐育”情绪传播机制,揭示其与生育政策背离的社会动因,主张构建多主体协同治理框架,强调需系统监测疾病相关网络情绪传播路径,遏制健康风险信息无序扩散。目前,社交网络已成为人们获取健康信息的主要渠道之一,但同时也成为健康负面信息和虚假信息的传播源,严重威胁公众的身心健康[41-42]。基于此,网络情绪治理需构建多层级协同机制,个体层面应强化数字健康素养教育,提升用户对高情绪负载信息的辨识与批判能力;平台层面需构建智能化内容审核机制,优化信息推荐算法权重配置以消解情绪极化,同时完善用户举报-响应闭环系统,并建立正向内容激励机制;社会层面应推动行业标准体系构建,引导社交媒体平台完善数字社会责任履行框架。
目前,医疗健康领域情感分析研究面临一定的技术挑战。首先,存在数据异质性障碍,医疗文本兼具专业术语密集、表述非结构化及隐私敏感等特征,导致特征工程构建困难且人工标注成本高昂;以癌症病人论坛为例,同一症状描述存在“疼痛”“不适”等数10种非标准表达,显著增加语义解析难度[22]。其次,模型跨域泛化局限,现有算法在跨疾病类型、跨文化语境场景下性能衰减,研究显示基于一种疾病论坛训练的模型在另一种疾病社区情绪识别的精准率和召回率有差异,凸显了临床语义迁移瓶颈[43]。最后,临床伦理风险的加剧,情绪分类错误可能引发健康信息传播偏差,叠加算法可解释性缺失,易导致临床决策信任危机[44]。针对上述挑战,系统性解决方案在于优化技术路径。首先,数据异质性障碍方面,需要构建医学-病人混合词典,集成标准术语库与病人社区语料库(如“疼痛→数字评分法”映射),开发具有医学语义推理能力的预训练模型;进行多模态特征融合架构和隐私保护下的高效标注[10,45]。其次,跨域泛化能力提升路径,构建临床认知图谱,建立跨疾病本体映射矩阵,量化不同专科疾病情绪表达的特征偏移度;在模型中间层植入可解释性模块,实时监测跨文化语境下的语义漂移;构建疾病谱系知识树,按临床相关性规划迁移学习路径[15, 39]。最后,在控制临床伦理风险体系方面,将深度学习预测结果与基于临床指南的符号推理系统进行逻辑校验,在损失函数中引入公平性约束项,通过反事实数据生成检测健康信息传播偏差;构建临床决策故事线可视化系统,展示从文本特征到编码的完整推理链条;构建医生-病人-数据科学家协同设计平台,确保技术开发与临床需求对齐[44,46]。因此,今后需加强多学科协作,形成“技术赋能护理实践,临床需求驱动技术迭代”的良性循环,推动医疗健康服务向数字化、智能化的高质量方向发展。
情感分析技术可为护理领域发展赋能,其在临床护理实践、护理管理优化、护理教育优化等方面的应用已取得显著成效。未来情感分析技术将向多模态融合、场景自适应、伦理合规化三大方向纵深发展,启示医护人员需要加强跨学科协作与护理信息化体系建设,提升护理服务的质量和效率,推动护理领域的智能化方向发展。

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doi: 10.12102/j.issn.1009-6493.2026.01.024
  • 接收时间:2025-02-23
  • 首发时间:2026-10-28
  • 出版时间:2026-01-10
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  • 收稿日期:2025-02-23
  • 修回日期:2025-11-19
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    1西湖大学医学院附属杭州市第一人民医院,浙江 310006

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2种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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