Measuring Emotional Expression with the Linguistic Inquiry and Word Count

2007 ◽  
Vol 120 (2) ◽  
pp. 263 ◽  
Author(s):  
Jeffrey H. Kahn ◽  
Renée M. Tobin ◽  
Audra E. Massey ◽  
Jennifer A. Anderson
Keyword(s):  
Emotional Expression ◽  
Word Count ◽  
Linguistic Inquiry

scholarly journals Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count

10.2196/18246 ◽  
2020 ◽  
Vol 4 (10) ◽  
pp. e18246
Author(s):  
Michelle McDonnell ◽  
Jason Edward Owen ◽  
Erin O'Carroll Bantum
Keyword(s):  
Emotional Expression ◽  
Reference Group ◽  
Negative Emotion ◽  
Mobile Apps ◽  
High Volume ◽  
Word Count ◽  
Web Based ◽  
Psychological Constructs ◽  
Linguistic Inquiry ◽  
Emotional Identification

Background Given the high volume of text-based communication such as email, Facebook, Twitter, and additional web-based and mobile apps, there are unique opportunities to use text to better understand underlying psychological constructs such as emotion. Emotion recognition in text is critical to commercial enterprises (eg, understanding the valence of customer reviews) and to current and emerging clinical applications (eg, as markers of clinical progress and risk of suicide), and the Linguistic Inquiry and Word Count (LIWC) is a commonly used program. Objective Given the wide use of this program, the purpose of this study is to update previous validation results with two newer versions of LIWC. Methods Tests of proportions were conducted using the total number of emotion words identified by human coders for each emotional category as the reference group. In addition to tests of proportions, we calculated F scores to evaluate the accuracy of LIWC 2001, LIWC 2007, and LIWC 2015. Results Results indicate that LIWC 2001, LIWC 2007, and LIWC 2015 each demonstrate good sensitivity for identifying emotional expression, whereas LIWC 2007 and LIWC 2015 were significantly more sensitive than LIWC 2001 for identifying emotional expression and positive emotion; however, more recent versions of LIWC were also significantly more likely to overidentify emotional content than LIWC 2001. LIWC 2001 demonstrated significantly better precision (F score) for identifying overall emotion, negative emotion, and anxiety compared with LIWC 2007 and LIWC 2015. Conclusions Taken together, these results suggest that LIWC 2001 most accurately reflects the emotional identification of human coders.

scholarly journals Twitter, time and emotions

2021 ◽  
Vol 8 (5) ◽  
pp. 201900
Author(s):  
Eric Mayor ◽  
Lucas M. Bietti
Keyword(s):  
Sentiment Analysis ◽  
Emotional Expression ◽  
Well Being ◽  
Temporal Variations ◽  
Word Count ◽  
Linguistic Inquiry Word Count ◽  
Sentiment Lexicon ◽  
Linguistic Inquiry ◽  
Expressed Emotions ◽  
Similarities And Differences

The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear circadian (24 h) and circaseptan (7-day) patterns. But to this point, such findings could be instrument-dependent as they rely exclusively on coding using the Linguistic Inquiry Word Count. Further, research has shown that self-referential content has higher relevance and meaning for individuals, compared with other types of content. Investigating the specificity of self-referential material in temporal patterns of emotional expression in tweets is of interest, but current research is based upon generic textual productions. The temporal variations of emotions shared in tweets through emojis have not been compared to textual analyses to date. This study hence focuses on several comparisons: (i) between Self-referencing tweets versus Other topic tweets, (ii) between coding of textual productions versus coding of emojis, and finally (iii) between coding of textual productions using different sentiment analysis tools (the Linguistic Inquiry and Word Count—LIWC; the Valence Aware Dictionary and sEntiment Reasoner—VADER and the Hu Liu sentiment lexicon—Hu Liu). In a collection of more than 7 million Self-referencing and close to 18 million Other topic content-coded tweets, we identified that (i) similarities and differences in terms of shape and amplitude can be observed in temporal trajectories of expressed emotions between Self-referring and Other topic tweets, (ii) that all tools feature significant circadian and circaseptan patterns in both datasets but not always, and there is often a correspondence in the shape of circadian and circaseptan patterns, and finally (iii) that circadian and circaseptan patterns obtained from the coding of emotional expression in emojis sometimes depart from those of the textual analysis, indicating some complementarity in the use of both modes of expression. We discuss the implications of our findings from the perspective of the literature on emotions and well-being.

scholarly journals Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count (Preprint)

2020 ◽  
Author(s):  
Michelle McDonnell ◽  
Jason Edward Owen ◽  
Erin O'Carroll Bantum
Keyword(s):  
Emotional Expression ◽  
Reference Group ◽  
Negative Emotion ◽  
Mobile Apps ◽  
High Volume ◽  
Word Count ◽  
Web Based ◽  
Psychological Constructs ◽  
Linguistic Inquiry ◽  
Emotional Identification

BACKGROUND Given the high volume of text-based communication such as email, Facebook, Twitter, and additional web-based and mobile apps, there are unique opportunities to use text to better understand underlying psychological constructs such as emotion. Emotion recognition in text is critical to commercial enterprises (eg, understanding the valence of customer reviews) and to current and emerging clinical applications (eg, as markers of clinical progress and risk of suicide), and the Linguistic Inquiry and Word Count (LIWC) is a commonly used program. OBJECTIVE Given the wide use of this program, the purpose of this study is to update previous validation results with two newer versions of LIWC. METHODS Tests of proportions were conducted using the total number of emotion words identified by human coders for each emotional category as the reference group. In addition to tests of proportions, we calculated F scores to evaluate the accuracy of LIWC 2001, LIWC 2007, and LIWC 2015. RESULTS Results indicate that LIWC 2001, LIWC 2007, and LIWC 2015 each demonstrate good sensitivity for identifying emotional expression, whereas LIWC 2007 and LIWC 2015 were significantly more sensitive than LIWC 2001 for identifying emotional expression and positive emotion; however, more recent versions of LIWC were also significantly more likely to overidentify emotional content than LIWC 2001. LIWC 2001 demonstrated significantly better precision (F score) for identifying overall emotion, negative emotion, and anxiety compared with LIWC 2007 and LIWC 2015. CONCLUSIONS Taken together, these results suggest that LIWC 2001 most accurately reflects the emotional identification of human coders.

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

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.

Emotion Word Use by Adolescents Who Stutter: An Exploratory Study

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

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

First Monday ◽  
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.

Linguistic Inquiry and Word Count (LIWC)

2012 ◽  
pp. 206-229 ◽  
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.

A Text Analysis of the Poems of Sylvia Plath

2011 ◽  
Vol 109 (1) ◽  
pp. 73-76 ◽  
Author(s):  
David Lester ◽  
Stephanie McSwain
Keyword(s):  
Computer Program ◽  
Text Analysis ◽  
Positive Emotions ◽  
Sylvia Plath ◽  
Word Count ◽  
Linguistic Inquiry

Changes in the words used in the poems of Sylvia Plath were examined using the Linguistic Inquiry and Word Count, a computer program for analyzing the content of texts. Major changes in the content of her poems were observed over the course of Plath's career, as well as in the final year of her life. As the time of her suicide came closer, words expressing positive emotions became more frequent, while words concerned with causation and insight became less frequent.

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