Linguistic Analysis in Personality Research (including the Linguistic Inquiry and Word Count)

Perinatal loss has a strong emotional impact on health professionals working in maternity units. We aimed to study the impact of this experience on health professionals’ language. We analyzed the answers of 162 health professionals (physicians and non-medical staff) who described their reactions to perinatal loss. A linguistic analysis was performed using the Linguistic Inquiry and Word Count software. Associations between language and burnout were studied. Words typical of a psychological shock reaction were used more by non-medical staff than by physicians. Participants who used pronouns, optimistic words, future tense verbs, and cognitive words registered lower levels of burnout. Clinical implications of the results are discussed.

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Writing About the Silence: Identifying the Language of Ostracism

Journal of Language and Social Psychology ◽

10.1177/0261927x19884599 ◽

2019 ◽

Vol 39 (5-6) ◽

pp. 751-763

Author(s):

Fabian Klauke ◽

Lena C. Müller-Frommeyer ◽

Simone Kauffeld

Keyword(s):

Cognitive Load ◽

Social Exclusion ◽

Social Inclusion ◽

Linguistic Analysis ◽

First Person ◽

Word Count ◽

Person Plural ◽

Linguistic Inquiry ◽

First Person Plural ◽

N 93

Autobiographical reports of episodes of ostracism and social inclusion were analyzed in two separate samples (German N = 93; English N = 243) using the Linguistic Inquiry and Word Count (LIWC) software. Recalling ostracism was associated with the use of more first-person singular pronouns, fewer first-person plural pronouns, and more complex language. These findings could reflect ostracism inducing a self-focus and putting high cognitive load on its targets. This study provides a first step to establish linguistic analysis as a tool for the research of social exclusion.

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Linguistic Analysis of Statements Concerning Paintings Viewed Under Different Instructions by Experts and Novices in the Visual Arts

Psychology of Language and Communication ◽

10.2478/plc-2019-0016 ◽

2019 ◽

Vol 23 (1) ◽

pp. 357-379

Author(s):

Piotr Jan Francuz ◽

Anna Szymańska ◽

Marcin Wojtasiński

Keyword(s):

19Th Century ◽

Visual Arts ◽

Cognitive Processes ◽

Linguistic Analysis ◽

Word Count ◽

Aesthetic Value ◽

Linguistic Inquiry Word Count ◽

The Arts ◽

Linguistic Inquiry ◽

Points Of View

Abstract This research aimed to perform linguistic analysis of the statements of experts and novices in the arts concerning figurative paintings from the 16th to 19th century of different aesthetic value under different instructions. The experts were selected based on a formal criterion of education in visual arts. Based on previous research, the paintings were divided into three groups: beautiful, not beautiful and controversial. The participants viewed them from different points of view defined by seven instructions. The Linguistic Inquiry Word Count (LIWC) was used to measure the connotation of statements in emotional and cognitive terms. Hypotheses, according to which the statements of novices are marked more with emotional, and those of experts more with cognitive processes, were only partially confirmed. It turned out that the emotional or cognitive connotation of statements concerning paintings is mostly modified by the point from which they are viewed and their aesthetic value.

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A Linguistic Analysis of Suicide-Related Twitter Posts

Crisis ◽

10.1027/0227-5910/a000443 ◽

2017 ◽

Vol 38 (5) ◽

pp. 319-329 ◽

Cited By ~ 38

Author(s):

Bridianne O'Dea ◽

Mark E. Larsen ◽

Philip J. Batterham ◽

Alison L. Calear ◽

Helen Christensen

Keyword(s):

Suicide Risk ◽

Linguistic Analysis ◽

Risk Assessments ◽

First Person ◽

Regression Analyses ◽

Word Count ◽

Online Risk ◽

Twitter Data ◽

Social Media Site ◽

Linguistic Inquiry

Abstract. Background: Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not. Aims: This study aims to characterize the linguistic profiles of suicide-related Twitter posts. Method: Using a dataset of suicide-related Twitter posts previously coded for suicide risk by experts, Linguistic Inquiry and Word Count (LIWC) and regression analyses were conducted to determine differences in linguistic profiles. Results: When compared with matched non-suicide-related Twitter posts, strongly concerning suicide-related posts were characterized by a higher word count, increased use of first-person pronouns, and more references to death. When compared with safe-to-ignore suicide-related posts, strongly concerning suicide-related posts were characterized by increased use of first-person pronouns, greater anger, and increased focus on the present. Other differences were found. Limitations: The predictive validity of the identified features needs further testing before these results can be used for interventional purposes. Conclusion: This study demonstrates that strongly concerning suicide-related Twitter posts have unique linguistic profiles. The examination of Twitter data for the presence of such features may help to validate online risk assessments and determine those in need of further support or intervention.

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A Mixed Methods Analysis of Parental Perspectives on Diagnosis and Prognosis of Neonatal Intensive Care Unit Graduates With Cerebral Palsy

Journal of Child Neurology ◽

10.1177/0883073820901412 ◽

2020 ◽

Vol 35 (5) ◽

pp. 336-343

Author(s):

Katherine Guttmann ◽

John Flibotte ◽

Sara B. DeMauro ◽

Holli Seitz

Keyword(s):

Intensive Care Unit ◽

Cerebral Palsy ◽

Content Analysis ◽

Intensive Care ◽

Neonatal Intensive Care Unit ◽

Thematic Analysis ◽

Neonatal Intensive Care ◽

Free Text ◽

Word Count ◽

Linguistic Inquiry

This study aimed to evaluate how parents of former neonatal intensive care unit patients with cerebral palsy perceive prognostic discussions following neuroimaging. Parent members of a cerebral palsy support network described memories of prognostic discussions after neuroimaging in the neonatal intensive care unit. We analyzed responses using Linguistic Inquiry and Word Count, manual content analysis, and thematic analysis. In 2015, a total of 463 parents met eligibility criteria and 266 provided free-text responses. Linguistic Inquiry and Word Count analysis showed that responses following neuroimaging contained negative emotion. The most common components identified through the content analysis included outcome, uncertainty, hope/hopelessness, and weakness in communication. Thematic analysis revealed 3 themes: (1) Information, (2) Communication, and (3) Impact. Parents of children with cerebral palsy report weakness in communication relating to prognosis, which persists in parents’ memories. Prospective work to develop interventions to improve communication between parents and providers in the neonatal intensive care unit is necessary.

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Emotion Word Use by Adolescents Who Stutter: An Exploratory Study

Perspectives on Fluency and Fluency Disorders ◽

10.1044/ffd23.1.6 ◽

2013 ◽

Vol 23 (1) ◽

pp. 6-14

Author(s):

Corrin G. Richels ◽

Rogge Jessica

Keyword(s):

Positive Emotion ◽

Negative Emotion ◽

Conversational Speech ◽

Word Count ◽

Word Use ◽

Emotion Words ◽

Emotion Word ◽

Linguistic Inquiry ◽

Positive Valence ◽

Analyze Data

Purpose: Deficits in the ability to use emotion vocabulary may result in difficulties for adolescents who stutter (AWS) and may contribute to disfluencies and stuttering. In this project, we aimed to describe the emotion words used during conversational speech by AWS. Methods: Participants were 26 AWS between the ages of 12 years, 5 months and 15 years, 11 months-old (n=4 females, n=22 males). We drew personal narrative samples from the UCLASS database. We used Linguistic Inquiry and Word Count (LIWC) software to analyze data samples for numbers of emotion words. Results: Results indicated that the AWS produced significantly higher numbers of emotion words with a positive valence. AWS tended to use the same few positive emotion words to the near exclusion of words with negative emotion valence. Conclusion: A lack of diversity in emotion vocabulary may make it difficult for AWS to engage in meaningful discourse about negative aspects of being a person who stutters

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Tweeting on dementia: A snapshot of the content and sentiment of tweets associated with dementia

First Monday ◽

10.5210/fm.v26i7.10452 ◽

2021 ◽

Author(s):

David Robertshaw ◽

Ivana Babicova

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Lewy Body Dementia ◽

Future Research ◽

Search Term ◽

Word Count ◽

Emotional Tone ◽

Search Terms ◽

Linguistic Inquiry ◽

World List

This study aimed to record and characterise tweets related to dementia, to investigate their content and sentiment. Data were extracted from Twitter over a period of six weeks during February and March 2019 and then analysed using Linguistic Inquiry and Word Count (LIWC) and AntWordProfiler. Using five search terms related to dementia, this study collected 860,383 tweets (more than 27 million words). Results have shown that out of all the collected tweets, 48.63 percent of tweets related to the search term ‘dementia’, 49.95 percent to ‘Alzheimer’s disease’ and the remainder related to frontotemporal dementia, Lewy Body dementia and vascular dementia. People wrote more positively and personally about the term ‘dementia’ than the other terms, and more technically regarding the term ‘Alzheimer’s disease’. All search terms had a negative emotional tone overall. Dementia and related terms are commonly discussed on Twitter. The overall negative emotional tone associated with all dementia related search terms suggests that dementia is still largely stigmatised and talked about negatively. Recommendations for future research include the development of a health world list or a dementia world list, and to consider how the results of this research inform social change interventions going forwards.

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Linguistic Inquiry and Word Count (LIWC)

Applied Natural Language Processing ◽

10.4018/978-1-60960-741-8.ch012 ◽

2012 ◽

pp. 206-229 ◽

Cited By ~ 24

Author(s):

Cindy K. Chung ◽

James W. Pennebaker

Keyword(s):

Group Dynamics ◽

Language Processing ◽

Behavioral Outcomes ◽

Content Word ◽

Analysis Tool ◽

Word Count ◽

Psychological States ◽

Psychological Measures ◽

Potential Applications ◽

Linguistic Inquiry

Linguistic Inquiry and Word Count (LIWC; Pennebaker, Booth, & Francis, 2007) is a word counting software program that references a dictionary of grammatical, psychological, and content word categories. LIWC has been used to efficiently classify texts along psychological dimensions and to predict behavioral outcomes, making it a text analysis tool widely used in the social sciences. LIWC can be considered to be a tool for applied natural language processing since, beyond classification, the relative uses of various LIWC categories can reflect the underlying psychology of demographic characteristics, honesty, health, status, relationship quality, group dynamics, or social context. By using a comparison group or longitudinal information, or validation with other psychological measures, LIWC analyses can be informative of a variety of psychological states and behaviors. Combining LIWC categories using new algorithms or using the processor to assess new categories and languages further extend the potential applications of LIWC.

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