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.

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.

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.

Bereavement after Suicide: A Study of Memorials on the Internet

2012 ◽  
Vol 65 (3) ◽  
pp. 189-194 ◽  
Author(s):  
David Lester
Keyword(s):  
Computer Program ◽  
Emotional Distress ◽  
The Self ◽  
The Internet ◽  
Significant Other ◽  
Word Count ◽  
Profound Impact ◽  
Survivors Of Suicide ◽  
Linguistic Inquiry

This study compared the written memorials on websites posted by survivors of suicide with those written by people who had lost a significant other from natural causes, using a computer program, the Linguistic Inquiry and Word Count. Thirteen significant differences were identified and, in addition, 13 differences that approach significance. Memorials written by survivors of suicide had longer sentences and used longer words. They had more death-related words, fewer references to the self or to the deceased (“you”), and more words reflective of anger and sadness. The results suggest the deaths from suicide had a more profound impact on the survivors than the natural deaths and results in greater emotional distress.

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 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.

scholarly journals Usage of humor to Overshadow Islamophobia in the World

2018 ◽  
Vol 02 (02) ◽  
Author(s):  
Farzana Kausar ◽  
Dr. Wajid Zulqarnain
Keyword(s):  
United States ◽  
Positive Emotions ◽  
Qualitative Content Analysis ◽  
The United States ◽  
Word Count ◽  
Muslim Identity ◽  
American Muslim ◽  
The World ◽  
Linguistic Inquiry ◽  
The Impact

After the 9/11 incident in the United States, a hatred gap arose between the Americans and Americans Muslims. Many Muslim stand-up comedians started their efforts to decrease this tension through humorous performances and comic screenplays. Also, Muslim comedians are neutralizing negative social discrimination between Muslims and another world to reduce the impact of Islamophobia in the world. The purpose of the study is to examine the part of Muslim comedians and the sentiments of the listeners about their stand-up humor videos. This study is the qualitative content analysis of comments on videos of two American Muslim comedians Negin Farsad and Obeidallah Dean. For data analysis, Linguistic Inquiry & Word Count (LIWC) used to analyses the positive emotions, negative emotions, and emotional tone. Findings exhibit that Muslim comedians are playing a critical part to assist the Muslim community handle with the effect of Islamophobia. The study also investigated that Muslim stand-up comedians are contributing to lessen the stereotyping discernments and disliked comments about Muslim identity globally. It is concluded that comedy has also helped in comforting the audiences that Arab and Muslims are not unpatriotic and adversarial to the United States or to another world.

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.

scholarly journals Analyzing Topic Differences, Writing Quality, and Rhetorical Context in College Students’ Essays Using Linguistic Inquiry and Word Count

2019 ◽  
Vol 6 (2) ◽  
pp. 107-118
Author(s):  
Roman Taraban ◽  
Abusal Khaleel
Keyword(s):  
Social Psychology ◽  
Academic Success ◽  
Expressive Writing ◽  
Text Analysis ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Small Samples ◽  
Word Count ◽  
Writing Quality ◽  
Linguistic Inquiry

Machine methods for automatically analyzing text have been investigated for decades. Yet the availability and usability of these methods for classifying and scoring specialized essays in small samples–as is typical for ordinary coursework–remains unclear. In this paper we analyzed 156 essays submitted by students in a first-year college rhetoric course. Using cognitive and affective measures within Linguistic Inquiry and Word Count (LIWC), we tested whether machine analyses could i) distinguish among essay topics, ii) distinguish between high and low writing quality, and iii) identify differences due to changes in rhetorical context across writing assignments. The results showed positive results for all three tests. We consider ways that LIWC may benefit college instructors in assessing student compositions and in monitoring the effectiveness of the course curriculum. We also consider extensions of machine assessments for instructional applications. 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. Carroll, D. W. (2007). Patterns of student writing in a critical thinking course: A quantitative analysis. Assessing Writing, 12, 213–227. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2-3), 259-284. Lord, S. P., Sheng, E., Imel, Z. E., Baer, J., & Atkins, D. C. (2015). More than reflections: Empathy in motivational interviewing includes language style synchrony between therapist and client. Behavior therapy, 46(3), 296-303. Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior research methods, instruments, & computers, 28(2), 203-208. Lunsford, A. A. (2016). St. Martin’s handbook (8th ed.): MLA supplement. Bedford/St. Martin’s Press. 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. Pennebaker, J. W. (2004). Theories, therapies, and taxpayers: On the complexities of the expressive writing paradigm. Clinical Psychology: Science and Practice, 11(2), 138-142. 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. Pennebaker, J. W., Chung, C. K., Frazee, J., Lavergne, G. M., & Beaver, D. I. (2014). When small words foretell academic success: The case of college admissions essays. PLoS ONE, 9(12), e115844. Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology 77(6), 1296-1312. Robertson, K., & Doig, A. (2010). An Empirical Investigation of Variations in Real‐Estate Marketing Language over a Market Cycle. Housing, Theory and Society, 27(2), 178-189. Robinson, R. L., Navea, R., & Ickes, W. (2013). Predicting final course performance from students’ written self-introductions: A LIWC analysis. Journal of Language and Social Psychology, 32(4), 469 – 479. Taraban, R., Pittman, J., Nalabandian, T., Yang, W. F. Z., Marcy, W. M., & Gunturu, S. M. (2019). Creating and testing specialized dictionaries for text analysis. East European Journal of Psycholinguistics, 6(1), 65-75. 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, 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.

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