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

Abstract

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

CLC Number:

TP311

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.

Figures/Tables 6

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）

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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

Text sentiment visual analysis technology and its application in humanities

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