Text Analysis in R

Abstract

Computational text analysis has become an exciting research field with many applications in communication research. It can be a difficult method to apply, however, because it requires knowledge of various techniques, and the software required to perform most of these techniques is not readily available in common statistical software packages. In this teacher's corner, we address these barriers by providing an overview of general steps and operations in a computational text analysis project, and demonstrate how each step can be performed using the R statistical software. As a popular open-source platform, R has an extensive user community that develops and maintains a wide range of text analysis packages. We show that these packages make it easy to perform advanced text analytics.

Topic Clustering of E-Cigarette Submissions Among Reddit Communities: A Network Perspective

“…To address the first aim of this study, we created an e-cigarette topics dictionary using the software package Quanteda (Welbers, Van Atteveldt, & Benoit, 2017). This dictionary categorized Reddit submissions based on seven different e-cigarette-related topics: (1) "advice," which refers to submissions about seeking information; (2) "build your own," which refers to submissions about e-cigarette parts or kits that can be used to build vaping devices; (3) "buying/selling," which refers to submissions about e-cigarettes as merchandise; (4) "drugs," which refers to submissions about the use of vaping devices for illicit purposes, such as vaping marijuana; (5) "e-juice," which refers to submissions about e-liquid or e-liquid flavors; (6) "health/safety," which refers to submissions about the various health effects associated with e-cigarettes; and (g) "tobacco," which refers to submissions containing tobacco-related content, including mentions of combustible cigarettes or nicotine.…”

Actionable Analytics on Software Requirement Specifications

“…Schubert et al [31] presented a novel methodology to model word significance and word affinity in a text and build the word cloud based on the derived dependency. Welbers et al [32] provided a summary of common steps and actions in a computational text analysis project and demonstrated how every step can be completed using the R statistical software.…”

“…This evaluation generates the weight of a term directly symmetric to its frequency in each document and inversely symmetric to its frequency to the set of documents. In a term-document matrix rows correspond to terms and columns correspond to documents in corpus [32]. Weighting of TDM is term frequency-inverse document frequency (TF-IDF) [14].…”

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

“…While data mining (DM) assumes that data is stored in a structured format, TM data needs no structured format. Thus, TM data requires the application of preprocessing operations to identify and extract features representative of natural language documents (Welbers, Van Atteveldt, & Benoit, 2017). Due to the importance of natural language processing in TM, the latter draws on the advances of other computer science disciplines, like data science, to achieve its objectives.…”

“…For this reason, and according to the scope of this work, it was decided to create a single dataset based on the fields described in Figure 4 by fusion of the two results. This involved a normalisation process: the conversion of all text into lowercase, thus transforming all words into a uniform form (Welbers et al, 2017). All text preprocessing was performed using the "NLP" (Hornik, 2017) and "tm" (Feinerer & Hornik, 2017) R packages.…”