Statistical inferences for polarity identification in natural language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work pro...

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Bibliographic Details
Published in:PloS one Vol. 13; no. 12; p. e0209323
Main Authors: Pröllochs, Nicolas, Feuerriegel, Stefan, Neumann, Dirk
Format: Journal Article
Language:English
Published: United States Public Library of Science 21.12.2018
Public Library of Science (PLoS)
Subjects:
Analysis
Behavior
Biology and Life Sciences
Biometry - methods
Content analysis
Decision Making
Dictionaries
Human behavior
Humans
Hypotheses
Hypothesis testing
Information processing
Language
Linear Models
Linguistics
Methods
Models, Psychological
Models, Statistical
Natural language processing
Perceptions
Polarity
Prediction models
Problem solving
Regularization
Sentiment analysis
Social Sciences
Statistics
Text analysis
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes LASSO regularization as a statistical tool to extract decisive words from textual content in order to study the reception of granular expressions in natural language. This differs from the usual use of the LASSO as a predictive model and, instead, yields highly interpretable statistical inferences between the occurrences of words and an outcome variable. Accordingly, the method suggests direct implications for the social sciences: it serves as a statistical procedure for generating domain-specific dictionaries as opposed to frequently employed heuristics. In addition, researchers can now identify text segments and word choices that are statistically decisive to authors or readers and, based on this knowledge, test hypotheses from behavioral research.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0209323