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.

该研究介绍了使用 R 进行情感分析的基本功能，并举例说明了其可能的用例，可以用于社交媒体平台和政治研究领域。

使用 R 进行情感分析：半自动化质性数据评估的自然语言处理

Sentiment Analysis with R: Natural Language Processing for Semi-Automated Assessments of Qualitative Data

This paper investigates the use of first person plural pronouns as a
rhetorical device in political speeches. We present an annotation schema for
disambiguating pronoun references and use our schema to create an annotated
corpus of debates from the German Bundestag. We then use our corpus to learn to
automatically resolve pronoun referents in parliamentary debates. We explore
the use of data augmentation with weak supervision to further expand our corpus
and report preliminary results.

本研究探讨了第一人称复数代词在政治演讲中作为修辞手法的使用。我们使用注释模式对代词指代进行消歧，并使用我们的模式来创建一个来自德国联邦议院辩论的带注释语料库。然后使用我们的语料库来学习自动解决议会辩论中的代词指称。我们探索使用弱监督的数据增强来进一步扩展我们的语料库，并报告初步结果。