文本情感可视分析技术及其在人文领域的应用

doi:10.11959/j.issn.2096-0271.2022050

大数据 ›› 2022, Vol. 8 ›› Issue (6): 56-73.doi: 10.11959/j.issn.2096-0271.2022050

• 专题：面向人文领域的大数据技术和方法 • 上一篇下一篇

文本情感可视分析技术及其在人文领域的应用

张伶俐¹, 褚琦凯², 王桂娟³^,⁴, 张巍瀚¹, 蒲慧¹, 宋振金¹, 吴亚东¹

¹ 四川轻化工大学计算机科学与工程学院，四川自贡 643000
² 四川轻化工大学自动化与信息工程学院，四川自贡 643000
³ 西南科技大学计算机科学与技术学院，四川绵阳 621000
⁴ 西南科技大学计算机信息工程学院，四川绵阳 621000

出版日期:2022-11-15 发布日期:2022-11-01
作者简介:张伶俐（1998- ），女，四川轻化工大学计算机科学与工程学院硕士生，主要研究方向为可视化与可视分析、自然语言处理等
褚琦凯（1996- ），男，四川轻化工大学自动化与信息工程学院硕士生，主要研究方向为可视化与可视分析等
王桂娟（1981- ），女，西南科技大学计算机科学与技术学院教师，西南科技大学信息工程学院博士生，主要研究方向为城市可视化、自动可视化
张巍瀚(1994- )，男，四川轻化工大学计算机科学与工程学院助教，主要研究方向为人机交互、数据可视化与可视分析
蒲慧（1997- ），女，四川轻化工大学计算机科学与工程学院硕士生，主要研究方向为可视化与可视分析、大数据分析与处理、深度学习
宋振金（1997- ），男，四川轻化工大学计算机科学与工程学院硕士生，主要研究方向为可视化与可视分析、大数据分析与处理、机器学习
吴亚东（1979- ），男，博士，四川轻化工大学计算机科学与工程学院教授、博士生导师，主要研究方向为可视化、可视分析、人机交互、虚拟现实
基金资助:
四川轻化工大学人才引进项目(2020RC20)

Text sentiment visual analysis technology and its application in humanities

Lingli ZHANG¹, Qikai CHU², Guijuan WANG³^,⁴, Weihan ZHANG¹, Hui PU¹, Zhenjin SONG¹, Yadong WU¹

¹ School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
² School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
³ School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621000, China
⁴ School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China

Online:2022-11-15 Published:2022-11-01
Supported by:
Talent Introduction Project of Sichuan University of Science and Engineering(2020RC20)

摘要/Abstract

摘要：

情感分析是对信息情感倾向的挖掘，主要用于舆情监测、商品评论分析以及信息检索等方面。随着社交媒体的快速发展，文本数据量呈现爆炸性增长，文本情感分析成为自然语言处理领域重要的研究热点之一。与此同时，由于情感数据具有海量、时变、非结构性、强关联性的特点，能够直观高效地呈现情感倾向的可视分析技术在这个领域得到广泛应用。回顾了近年来的情感可视分析研究，从表现形式——“主题词”“关联”“演变”“时空分布”4个方面阐述文本情感可视分析方法，并对未来情感分析技术及文本情感可视分析研究进行展望。

关键词: 文本情感分析, 可视分析, 数据分析, 机器学习

Abstract:

Sentiment analysis is the mining of information sentiment tendency, which is mainly used for public opinion monitoring, commodity review analysis, and information retrieval.With the rapid development of social media, the volume of text data has shown explosive growth, and text sentiment analysis has become one of the important research hotspots in the field of natural language processing.At the same time, due to the characteristics of massive, time-varying, unstructured and strongly correlated sentiment data, visual analysis techniques that can present sentiment tendencies intuitively and efficiently are widely used in this field.The recent research on visual analysis of sentiment was reviewed, and according to the presentation form “topic words”, “association”, “evolution”, “spatial and temporal distribution” four aspects of text sentiment visual analysis methods were described, and future sentiment analysis techniques as well as text sentiment visual analysis research were foreseen.

Key words: text sentiment analysis, visual analysis, data analysis, machine learning

中图分类号:

TP311

张伶俐, 褚琦凯, 王桂娟, 张巍瀚, 蒲慧, 宋振金, 吴亚东. 文本情感可视分析技术及其在人文领域的应用[J]. 大数据, 2022, 8(6): 56-73.

Lingli ZHANG, Qikai CHU, Guijuan WANG, Weihan ZHANG, Hui PU, Zhenjin SONG, Yadong WU. Text sentiment visual analysis technology and its application in humanities[J]. Big Data Research, 2022, 8(6): 56-73.

图/表 6

图1

表1

近10年前人进行的文本情感分析研究及其实验内容"

分类	模型	数据集来源	分类类别	准确率	F1值	优点	局限性
基于情感词	6种情感词典^[16]	1 642篇关于“空气污染能导致肥	三分类（积极、中性、	积极：72.6%	-	准确反映非结构性	词典具有局限性，
典的文本情		胖”主题的微博文本	消极）	中性：73.7%		特征^[18]，高效，对适	部分词典构建困难
感分析				消极：68.3%		配性文本准确率高
	6种情感词典^[16]	1 839篇关于“高价救护车”主题的		积极：67.7%	-
		微博文本		中性：74.3%
				消极：63.8%
	6种情感词典^[16]	1 258篇关于“分享自行车，文明旅		积极：68.3%	-
		行”主题的微博文本		中性：74.8%
				消极：59.3%
	基于语料库的词典	电影评论数据集	二分类（积极、消极）	80.2%	-
	并用于专门词汇的
	情感分析^[17]
基于机器学	SMO^[19]	推特（Twitter）的食品公司的商品	三分类（积极、中性、	72.33%	-	相较于基于情感词	无法将文本中词汇
习的文本情		评论数据	消极）			典的方法，基于机器	之间的关联程度进
感分析	WCT-Bi-LSTM^[20]	豆瓣电影评论	三分类（积极、中性、	82.57%	-	学习的方法更简单，	行结合；训练数据
			消极）			且可根据不同文本	的标签准确与否将
		京东评论数据		70.98%	-	特征选取和融合不同	会对情感分类结果
						的情感分类模型^[18]	有较大的影响；按
							照文本信息对文本
							中的相同字进行不
							同的词向量表示会
							降低分析的准确性
引入特殊模	BERT-DCA^[21]	中文情感挖掘酒店评论语料	二分类（积极、消极）	-	93.98%	将文本语段中各词	模型数据需求量
型的文本情						汇之间的关联程度	大且耗时长^[18]；模
感分析		在豆瓣电影平台爬取的近30部电影	二分类（积极、消极）	-	94.48%	进行整合；按照文本	型对使用环境要求
		的评论				信息对文本中的相	较高
	STBi-LSTM^[22]	SemEval 2018	-	-	67.55%	同字进行不同的词向
						量表示；可将临近领
	ATAE-LSTM^[23]	SemEval 2014 Task 4	三分类（积极、中性、	84.0%	-	域的分析模型进行
			消极）			迁移
			二分类（积极、消极）	89.9%	-
	AEN-BERT^[24]	SemEval 2014 Task 4	三分类（积极、中性、	83.12%	73.96%
			消极）

表1

表2

图2

图3

图4

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分类	代表方法	示例论文	优点	局限性
情感主题词可视分析	词云图	[35-37]	直观展现文本关键词及其情感信息	展示效果较为局限
	星座图，环形图	[38-39]	多层次展现文本情感信息	图形相对复杂，较难理解
情感关联可视分析	节点链接图	[39-42]	展现情感的关联性	数据关系复杂，易造成视觉混淆
	树图	[37,43-45]	展现情感的层次性组织关系	无法有效展示数据的多属性内容
情感演变可视分析	折线图、散点图、气泡图、河流图	[46-49]	展现情感信息的时间趋势变化，突出情感变化特征	对于带时间属性的多变量关联情感可视分析具有挑战性
	多层圆环图	[50-51]	展现情感信息的周期性趋势变化，节省空间	数据展示易造成视觉差误解
	日历图	[52-53]	直观展示时间变化下不同情感信息的对比	不适用于数据量较大的数据集
情感时空分布可视分析	分类点地图	[54-57]	情感信息的离散性展现	不适用于数据量较大的情感信息的展现
	聚类点地图	[58-60]	有效表现数据分布，避免产生视觉混淆	多属性表达具有挑战性
	区域地图	[61-62]	展现各区域的情感属性	不利于数据的离散性展现

文本情感可视分析技术及其在人文领域的应用

Text sentiment visual analysis technology and its application in humanities

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