quanteda: Quantitative Analysis of Textual Data
Published 7 April 2015Open access
Kenneth Benoit, Kohei Watanabe, H. P. Wang, Paul Nulty, Adam Obeng, Stefan Müller
Citations51
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Keywords
Computer ScienceSocial Sciences
The Journal of Open Source Softwarequanteda: An R package for the quantitative analysis of textual data
1,323 Citations2018Kenneth Benoit, Kohei Watanabe +5 more
While using quanteda requires R programming knowledge, its API is designed to enable powerful, efficient analysis with a minimum of steps, which lowers the barriers to learning and using NLP and quantitative text analysis even for proficient R programmers.
Political CommunicationAffective News: The Automated Coding of Sentiment in Political Texts
585 Citations2012Lori Young, Stuart Soroka
The objective here is to outline and validate a new automated measurement instrument for sentiment analysis in political texts using a dictionary-based approach consisting of a simple word count of the frequency of keywords in a text from a predefined dictionary.