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

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.

The Content of Suicide Notes from Attempters and Completers

Crisis ◽  
2007 ◽  
Vol 28 (2) ◽  
pp. 102-104 ◽  
Author(s):  
Lori D. Handelman ◽  
David Lester
Keyword(s):  
Text Analysis ◽  
Positive Emotions ◽  
Analysis Program ◽  
Word Count ◽  
Future Tense ◽  
Suicide Notes ◽  
Program Notes ◽  
Linguistic Inquiry

Abstract. A study of the content of suicide notes from attempted suicides and completed suicides was conducted using the Linguistic Inquiry and Word Count (LIWC) text analysis program. Notes from completed suicides had fewer metaphysical references, more future tense verbs, more social references (to others) and more positive emotions than did the notes from attempted suicides. The implications of these results were discussed.

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.

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

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 ◽  
Linguistic Inquiry Word Count ◽  
Linguistic Inquiry

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.

scholarly journals Creating and Testing Specialized Dictionaries for Text Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 65-75 ◽  
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 ◽  
Linguistic Inquiry

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.

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

2021 ◽  
Author(s):  
Sanaz Aghazadeh ◽  
Kris Hoang ◽  
Bradley Pomeroy
Keyword(s):  
Text Analysis ◽  
Affective States ◽  
Word Count ◽  
Psycholinguistic Research ◽  
Psychological Constructs ◽  
Written Responses ◽  
Linguistic Inquiry Word Count ◽  
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.

An Analysis of Linguistic Styles by Inferred Age in TV Dramas

2006 ◽  
Vol 99 (2) ◽  
pp. 351-356 ◽  
Author(s):  
Chang H. Lee ◽  
Jongmin Park ◽  
Young Seok Seo
Keyword(s):  
Cognitive Development ◽  
Spoken Language ◽  
Analysis Program ◽  
Word Count ◽  
Negative Stereotypes ◽  
Psychological Variables ◽  
Language Analysis ◽  
Linguistic Inquiry ◽  
Aging People ◽  
The Relationship

A language analysis program, Linguistic Inquiry and Word Count (LIWC), was successful in identifying various psychological variables. This study investigated the relationship between spoken language and age inferred from drama scripts of 162 characters, analyzed by the Korean-LIWC across 4 age categories (10–19, 20–39, 40–59, and 60–79 years). Analysis indicated that younger characters use fewer phrases, morphemes, nouns, auxiliary words, and adverbs than older characters, suggesting less cognitive development of younger characters. In addition, younger characters used less positive words for emotion and achievement than older characters. These data appear contrary to the negative stereotypes of aging people.

Understanding the Role of Language in Conflict

2019 ◽  
Vol 64 (1) ◽  
pp. 97-117 ◽  
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.

scholarly journals Linguistic Markers of Self-Disclosure: Using YouTube Coming Out Videos to Study Disclosure Language

2020 ◽  
Author(s):  
Patrick Charles Doyle ◽  
William Keith Campbell
Keyword(s):  
Coming Out ◽  
Text Analysis ◽  
Word Count ◽  
Analysis Software ◽  
Hiv Diagnosis ◽  
Self Disclosure ◽  
Linguistic Markers ◽  
Behavior Outcomes ◽  
Youtube Videos ◽  
Linguistic Inquiry

Traditional attempts at measuring self-disclosure actually measure self-reported perceptions of disclosure, which is conflated with individual difference characteristics, or rely on trained coders, which is time-consuming. Across a pilot and two studies and using a known-groups paradigm with repeated transcripts from YouTube videos in which creators express a concealable stigmatized identity(LGBTQ, HIV diagnosis, or mental illness), we measured self-disclosure with the Linguistic Inquiry and Word Count and found support for the utility of text-based analyses for operationalization of disclosure. This output was correlated with trained coders’ ratings and was effective for predicting audience behavior outcomes, including reciprocal disclosure. Finally, we discuss the utility of text-analysis software for theoretical and applied work.

Preliminary Analysis of Language Styles in a Sample of Schizophrenics

2007 ◽  
Vol 101 (2) ◽  
pp. 392-394 ◽  
Author(s):  
Chang H. Lee ◽  
Myungju Lee ◽  
Sungwoo Ahn ◽  
Kyungil Kim
Keyword(s):  
Preliminary Analysis ◽  
Language Use ◽  
Emotional Experience ◽  
Analysis Program ◽  
Word Count ◽  
Personal Pronouns ◽  
Normal People ◽  
Language Analysis ◽  
Linguistic Inquiry ◽  
Language Styles

Language use of schizophrenics and normal people was compared by applying the language analysis program, Korean Linguistic Inquiry and Word Count. Participants were asked to write a story about the most emotional experience of their lives on A4 size paper. 28 schizophrenics ( M age: 26 yr.) and 32 normal people (Ai age: 23 yr.) participated. Analysis showed normal people used more words about jobs and achievements and fewer words about sex and food. The schizophrenics used fewer pronouns, personal pronouns, and adverbs than the normal group. Some aspects of clinical mechanism are manifest in language uses.

scholarly journals Predicting Educational Background using Text Mining

2021 ◽  
Author(s):  
Rense Corten ◽  
Shiva Nadi ◽  
Laurence Frank
Keyword(s):  
Educational Background ◽  
Word Count ◽  
Level Of Education ◽  
Written Text ◽  
Different Types ◽  
Linguistic Inquiry ◽  
Public Dataset ◽  
Style Of Writing ◽  
Platform Economy ◽  
Better Than

We examine to what extent educational background can be inferred from written text, assuming that educational levels are associated with the style of writing and use of language. Using a large public dataset of almost 60000 dating profiles, containing written text for each profile, we look for a methodology to measure author style. We focus on education and essays fields in each profile from which we try to identify relevant features of written text that reveal the level of education of authors behind texts. Using different types of extracted features, we explore the level of education within three approaches: (i) classifying the level of education to elementary or higher education using lexical features; (ii) using Linguistic Inquiry and Word Count (LIWC) features; (iii) combining LIWC features and lexical features. For classification, we rely on regularized logistic regression. The joint model which uses both lexical and LIWC features predicts the education level better than other text representation models, but the contribution of LIWC is marginal. Our results may not only be useful in the context of the platform economy and online markets, also more generally to researchers who need to rely on written text as an indicator of educational background.

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