Using LIWC to Analyze Participants’ Psychological Processing in Accounting JDM Research

Control Procedures ◽

Linguistic Inquiry ◽

Psychological Processing

This paper provides methodological guidance for judgment and decision-making (JDM) researchers in accounting who are interested in using the Linguistic Inquiry Word Count (LIWC) text analysis program to analyze research participants’ written responses to open-ended questions. We discuss how LIWC’s measures of psychological constructs were developed and validated in psycholinguistic research. We then use data from an audit JDM study to illustrate the use of LIWC to guide researchers in identifying suitable measures, performing quality control procedures, and reporting the analysis. We also discuss research design considerations that will strengthen the inferences drawn from LIWC analysis. The paper concludes with examples where LIWC analysis has the potential to reveal participants’ deep, complex, effortful psychological processing and affective states from their written responses.

Linguistic Markers in Women’s Discussions on Miscarriage and Abortion Illustrate Psychological Responses to Their Experiences

Journal of Language and Social Psychology ◽

10.1177/0261927x20965643 ◽

2020 ◽

pp. 0261927X2096564

Author(s):

Kate G. Blackburn ◽

Weixi Wang ◽

Rhea Pedler ◽

Rachel Thompson ◽

Diana Gonzales

Keyword(s):

Emotion Regulation ◽

Text Analysis ◽

The Other ◽

Psychological Responses ◽

Word Count ◽

Future Directions ◽

Trauma Research ◽

Linguistic Markers ◽

This study analyzed thousands of women’s online conversations in relation to their miscarriage or abortion experiences, classified as unplanned and planned traumas, respectively. Linguistic Inquiry Word Count text analysis revealed that people experiencing a planned trauma use distancing language patterns in higher frequency and engage in emotion regulation more than those who experienced trauma unexpectedly. On the other hand, planned trauma conversations used more self-focused language and more social-based language. Implications and future directions for trauma research are discussed.

A Text Analysis of the Poems of Sylvia Plath

Psychological Reports ◽

10.2466/09.12.28.pr0.109.4.73-76 ◽

2011 ◽

Vol 109 (1) ◽

pp. 73-76 ◽

Cited By ~ 3

Author(s):

David Lester ◽

Stephanie McSwain

Keyword(s):

Computer Program ◽

Text Analysis ◽

Positive Emotions ◽

Sylvia Plath ◽

Word Count ◽

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.

Creating and Testing Specialized Dictionaries for Text Analysis

East European Journal of Psycholinguistics ◽

10.29038/eejpl.2019.6.1.rta ◽

2019 ◽

Vol 6 (1) ◽

pp. 65-75 ◽

Cited By ~ 1

Author(s):

Роман Тарабань ◽

Джесіка Піттман ◽

Талін Налбандян ◽

Winson Fu Zun Yang ◽

Вільям Марсі ◽

...

Keyword(s):

Text Analysis ◽

Latent Dirichlet Allocation ◽

Naive Bayes ◽

Qualitative Content Analysis ◽

Naïve Bayes ◽

Computer Application ◽

Word Count ◽

Constructive Feedback ◽

Discourse Processes ◽

Practitioners in many domains–e.g., clinical psychologists, college instructors, researchers–collect written responses from clients. A well-developed method that has been applied to texts from sources like these is the computer application Linguistic Inquiry and Word Count (LIWC). LIWC uses the words in texts as cues to a person’s thought processes, emotional states, intentions, and motivations. In the present study, we adopt analytic principles from LIWC and develop and test an alternative method of text analysis using naïve Bayes methods. We further show how output from the naïve Bayes analysis can be used for mark up of student work in order to provide immediate, constructive feedback to students and instructors. References Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022. Boot, P., Zijlstra, H., & Geenen, R. (2017). The Dutch translation of the Linguistic Inquiry and Word Count (LIWC) 2007 dictionary. Dutch Journal of Applied Linguistics, 6(1), 65-76. Chung, C. K., & Pennebaker, J. W. (2008). Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language. Journal of research in personality, 42(1), 96-132. Hsieh, H-F., & Shannon, S. E. (2005).Three approaches to qualitative content analysis. Qualitative health research, 15(9), 277-1288. Kintsch, W. (1998). Comprehension: A paradigm for cognition. New York: Cambridge University Press. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2-3), 259-284. Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers, 28(2), 203-208. Massó, G., Lambert, P., Penagos, C. R., & Saurí, R. (2013, December). Generating New LIWC Dictionaries by Triangulation. In Asia Information Retrieval Symposium (pp. 263-271). Springer, Berlin, Heidelberg. Newman, M., Groom, C.J., Handelman, L.D., & Pennebaker, J.W. (2008). Gender differences in language use: An analysis of 14,000 text samples. Discourse Processes, 45(3), 211-236. Pennebaker, J.W., Boyd, R.L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC 2015. Austin, TX: University of Texas at Austin. Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology, 29(1), 24-54. Van Wissen, L., & Boot, P. (2017, September). An Electronic Translation of the LIWC Dictionary into Dutch. In: Electronic lexicography in the 21st century: Proceedings of eLex 2017 Conference. (pp. 703-715). Lexical Computing.

Analysing literacy and other psychological tendencies using linguistic profile in English expressive writing: Are students able but unwilling to write?

Indonesian Journal of Applied Linguistics ◽

10.17509/ijal.v9i1.12698 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Bonita Lee ◽

Annisa Fitria ◽

Henndy Ginting

Keyword(s):

Critical Literacy ◽

Expressive Writing ◽

Business Students ◽

Word Count ◽

Significant Discrepancy ◽

Men And Women ◽

Global Competitiveness ◽

Linguistic Inquiry ◽

Global Population

The use of English in educational settings has become quite common in order to achieve global competitiveness. Given this fact, students are required to be fluent both in oral and written English. Unfortunately, the significant discrepancy is often found between the two. Students seemed to struggle when asked to elaborate their ideas in writing. With that in mind, this study would elaborate on the linguistic properties of students’ writings in order to understand the linguistic processes affecting such a discrepancy. Writings from a total of 205-business students were analysed using Linguistic Inquiry Word Count (LIWC2015) focusing on the linguistic and grammatical properties such as word counts, tenses associated words, adjectives, adverbs and so on. We found that our samples’ writing profile was significantly different from those of LIWC2015, especially in properties such word counts, six-letter words, verb and adjectives, as well as the use of I-related pronoun. For example, we found that our sample used a lot more difficult words while wrote less than half of the global population, suggesting their ability as well as unwillingness to write at the same time. With this main finding, we concluded that students come short in terms of critical literacy. In addition to that, we would also discuss the potential psychological implications (narcissistic tendency) as well as the differences between men and women styles in writing.

Proceedings of the 2015 International Conference on Social Science and Technology Education ◽

A Case Study of Text and Discourse Based on Juku, Coh-Metrix, and Linguistic Inquiry Word Count (LIWC)

10.2991/icsste-15.2015.261 ◽

2015 ◽

Author(s):

Liu Jia ◽

Shen Ying

Keyword(s):

Word Count ◽

Understanding the Role of Language in Conflict

American Behavioral Scientist ◽

10.1177/0002764219859626 ◽

2019 ◽

Vol 64 (1) ◽

pp. 97-117 ◽

Cited By ~ 1

Author(s):

William A. Donohue ◽

Qi Hao ◽

Richard Spreng ◽

Charles Owen

Keyword(s):

Text Analysis ◽

Word Count ◽

Data Set ◽

Open Source Data ◽

Communication Events ◽

Source Data ◽

Linguistic Inquiry ◽

Mind Set ◽

The Mind

The purpose of this article is to illustrate innovations in text analysis associated with understanding conflict-related communication events. Two innovations will be explored: LIWC (Linguistic Inquiry and Word Count), the text modeling program from the open-source data analysis software program R, and SPSS Modeler. The LIWC analysis revisits the 2009 study by Donohue and Druckman and the 2014 study by Donohue, Liang, and Druckman focusing on text analyses of the Oslo I Accords between the Palestinians and Israelis to illustrate this approach. The R and SPSS modeling of text analysis use the same data set as the LIWC analysis to provide a different set of pictures associated with each leader’s rhetoric during the period in which the Oslo I accords were being negotiated. Each innovation provides different insights into the mind-set of the two groups of leaders as the secret talks were emerging. The implications of each approach in establishing an understanding of the communication exchanges are discussed to conclude the article.

A computer’s understanding of literature

Scientific Study of Literature ◽

10.1075/ssol.1.1.03gra ◽

2011 ◽

Vol 1 (1) ◽

pp. 24-33 ◽

Cited By ~ 4

Author(s):

Arthur C. Graesser ◽

Nia Dowell ◽

Christian Moldovan

Keyword(s):

Computational Linguistics ◽

Discourse Processing ◽

Literary Texts ◽

Word Count ◽

Social Scientists ◽

Computer Tools ◽

Word Concreteness ◽

Political Speeches ◽

Everyone agrees that a computer could never understand and appreciate literature, but the fields of computational linguistics and discourse processing have made important advances in automatic detection of language and discourse characteristics. We have analyzed literary texts and political speeches with two computer tools, namely Coh-Metrix and Linguistic Inquiry Word Count (LIWC). Coh-Metrix provides hundreds of measures that funnel into 5 principal components: word concreteness, syntactic simplicity, referential cohesion, deep cohesion, and narrativity. LIWC classifies words on 80 categories, such as first person pronouns, negative emotions, and social words. This paper illustrates how computer tools can unveil new insights about literature and can empirically test claims by literary scholars and social scientists. Our approach offers a computational science of literature.

Machine-translated texts as an alternative to translated dictionaries for LIWC

10.31219/osf.io/tsc36 ◽

2021 ◽

Author(s):

Peter Boot

Keyword(s):

Machine Translation ◽

Text Analysis ◽

Analysis Program ◽

Word Count ◽

Alternative Approach ◽

Linguistic Inquiry ◽

Better Than ◽

Dictionary Translation

Linguistic Inquiry and Word Count (LIWC) is a text analysis program developed by James Pennebaker and colleagues. At the basis of LIWC is a dictionary that assigns words to categories. This dictionary is specific to English. Researchers who want to use LIWC on non-English texts have typically relied on translations of the dictionary into the language of the texts. Dictionary translation, however, is a labour-intensive procedure. In this paper, we investigate an alternative approach: to use Machine Translation (MT) to translate the texts that must be analysed into English, and then use the English dictionary to analyse the texts. We test several LIWC versions, languages and MT engines, and consistently find the machine-translated text approach performs better than the translated-dictionary approach. We argue that for languages for which effective MT technology is available, there is no need to create new LIWC dictionary translations.

Expressions of doubt and trust in online user reviews

10.31234/osf.io/x9zqt ◽

2019 ◽

Author(s):

Anthony M Evans ◽

Olga Stavrova ◽

Hannes Rosenbusch

Keyword(s):

Conflicts Of Interest ◽

Online Reviews ◽

Word Count ◽

Linguistic Complexity ◽

Psychological Variables ◽

User Reviews ◽

Online User Reviews ◽

How do expressions of doubt affect trust in online reviews? Previous research leads to conflicting predictions: some studies find that people trust confident advisors more than doubtful advisors, whereas others find doubtful advisors are trusted more, especially when advisors have salient conflicts-of-interest. We tested the effects of doubt in the Yelp Open Dataset (N = 5.9 million user reviews). Reviews were coded using the Linguistic Inquiry Word Count (LIWC) software, which contains two dictionaries related to doubt, tentativeness and (lack of) certainty. Doubtful reviews were more likely to be seen as useful, and this result was robust when controlling for other psychological variables, as well as review length and linguistic complexity. The beneficial consequences of expressing doubt were strongest for positive (5-star) reviews, suggesting that doubt may mitigate concerns about the veracity of overly positive reviews. The present study emphasizes the advantages of expressing doubt.

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

JMIR Formative Research ◽

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.