论文标题
与R：自然语言处理的情感分析，用于对定性数据的半自动化评估
Sentiment Analysis with R: Natural Language Processing for Semi-Automated Assessments of Qualitative Data
论文作者
论文摘要
情感分析是自然语言处理和计算语言学领域的子学科，可用于自动化或半自动化文档分析。这些分析的目的之一是将表达的态度承认为积极或负面的，因为它可以包含在社交媒体平台上的评论，政治文件和演讲以及虚构和非虚构的文本中。关于在社交媒体平台上的评论分析，这是关于社交媒体网络数据半自动筛选的先前教程的延伸。关于社交媒体评论以及关于虚构和非虚构文本的横断面观点的纵向观点，例如整个书籍和图书馆可能会导致广泛的文本文档。通过使用可接受的评估者间可靠性，可以通过使用情感分析来简化和加速他们的分析。因此，本教程介绍了用R执行情感分析的基本功能，并解释了如何逐步分析文本文档 - 无论其潜在格式如何。详细描述了所有先决条件和步骤，并在GitHub上提供相关的代码。对两个政治演讲的比较说明了可能的用例。
Sentiment analysis is a sub-discipline in the field of natural language processing and computational linguistics and can be used for automated or semi-automated analyses of text documents. One of the aims of these analyses is to recognize an expressed attitude as positive or negative as it can be contained in comments on social media platforms or political documents and speeches as well as fictional and nonfictional texts. Regarding analyses of comments on social media platforms, this is an extension of the previous tutorial on semi-automated screenings of social media network data. A longitudinal perspective regarding social media comments as well as cross-sectional perspectives regarding fictional and nonfictional texts, e.g. entire books and libraries, can lead to extensive text documents. Their analyses can be simplified and accelerated by using sentiment analysis with acceptable inter-rater reliability. Therefore, this tutorial introduces the basic functions for performing a sentiment analysis with R and explains how text documents can be analysed step by step - regardless of their underlying formatting. All prerequisites and steps are described in detail and associated codes are available on GitHub. A comparison of two political speeches illustrates a possible use case.
