Sentiment Analysis with R: Natural Language Processing for
Semi-Automated Assessments of Qualitative Data
- URL: http://arxiv.org/abs/2206.12649v1
- Date: Sat, 25 Jun 2022 13:25:39 GMT
- Title: Sentiment Analysis with R: Natural Language Processing for
Semi-Automated Assessments of Qualitative Data
- Authors: Dennis Klinkhammer
- Abstract summary: This tutorial introduces the basic functions for performing a sentiment analysis with R and explains how text documents can be analysed step by step.
A comparison of two political speeches illustrates a possible use case.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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