大数据

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文本情感可视分析研究综述

张伶俐1,吴亚东1,褚琦凯2,王桂娟3,张巍瀚1,蒲慧1,宋振金1   

  1. 1. 四川轻化工大学计算机科学与工程学院  自贡 643000;

    2. 四川轻化工大学自动化与信息工程学院  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

  • 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。 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。 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,西南科技大学信息工程学院博士生,西南科技大学计算机科学与技术学院助教,主要研究方向为城市可视化、自动可视化。

A Review of Research on Visual Analysis of Textual Sentiment

Zhang Lingli1, Wu Yadong1, Chu Qikai2, Wang Guijuan3, Zhang Weihan1, Pu Hui1, Song Zhenjin1   

  1. 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

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。同时由于文本数据具有海量、时变、非结构性、强关联性的特点,能够直观高效呈现情感倾向的可视分析技术在这个领域得到广泛应用。本文回顾了近年来的情感可视分析研究,根据表现形式——“主题词”、“关联”、“演变”、“时空分布”四个方面阐述文本情感可视分析方法,并对未来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 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 text data, visual analysis techniques that can intuitively and efficiently present sentiment tendencies are widely used in this field. This paper reviews the recent research on visual analysis of sentiment, and according to the presentation form——"Topic words", "association”, "evolution", "spatial and temporal distribution"" four aspects of text sentiment visual analysis methods are described, and a vision for future research on visual analysis of textual emotions.

Key words: sentiment analysis, visual analysis, data analysis, machine Learning

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