Performing Multilingual Analysis With Linguistic Inquiry and Word Count 2015 (LIWC2015). An Equivalence Study of Four Languages

Language in the Teaching Operating Room: Expressing Confidence Versus Community

OBJECTIVE

Previous work has analyzed residency letters of recommendation for agentic and communal language, but this has not been applied to spoken language. Our objective was to analyze intraoperative spoken language by attending and resident surgeons for the use of agentic and communal language.

DESIGN

We completed a linguistic inquiry and word count (LIWC) analysis on 16 operating room transcripts (total time 615 minutes) between attendings and resident surgeons for categories associated with agentic and communal speech. Wilcoxon signed rank and Mann-Whitney U tests were used to compare attending versus resident and male versus female speech patterns for word count; "I," clout, and power (agentic categories); and "we," authentic, social (communal categories).

SETTING

Midwestern academic university teaching hospital.

PARTICIPANTS

Sixteen male (9 attendings, 7 residents) and 16 female (7 attendings, 9 residents) surgeons, from 6 surgical specialties, most commonly from General Surgery.

RESULTS

Attending surgeons used more words per minute than residents (40.01 vs 16.92, p < 0.01), were less likely to use "I" (3.18 vs 5.53, p < 0.01), and spoke more language of "clout" (75.82 vs 55.47, p < 0.01). There were no significant differences between attendings and residents in use of analytic speech (23.72 vs 24.67, p = 0.32), "causation" (1.20 vs 1.08, p = 0.72), or "cognitive processing" (10.20 vs 10.54, p = 0.74). Residents used more speech with "emotional tone" (92.91 vs 79.92, p = 0.03), "positive emotion" (4.98 vs 3.86, p = 0.04), more "assent" language (4.89 vs 3.09, p < 0.01), and more "informal" language (9.27 vs 6.77, p < 0.01). There were no gender differences, except for male residents speaking with greater certainty than female residents, although by less than 1% of the total word count.

CONCLUSIONS

In the operating room, attending surgeons were more likely to use agentic language compared to resident surgeons based on LIWC analysis. These differences did not depend on gender and likely relate to surgeon experience and confidence, learning versus teaching, and power dynamics.

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure

Introduction

The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts.

Aim

This study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count).

Materials and methods

A sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them.

Assessment tools

(1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 - Linguistic Inquiry and Word Count - a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use.

Results

Depressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form - not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money.

Conclusion

Depressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer ( https://lingualyzer.com ), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface.

Personality and Word Use: Study on Czech Language and the Big Five

The study is a follow-up to three published anglophone researches examining the relation between the use of linguistic categories and personality characteristics as outlined in the Big Five model, with the purpose of replicating these and elaborating for the Czech language. The comparative research study in Czech focuses on analysis of both grammatical and semantic variables in six types of text (written and oral), produced by N = 200 participants. Within the study, there were six confirmed relations, however, these appear only in certain types of text. The results show not only an essential role of the text register, but they also allow us to evaluate the universality of findings of studies in English in comparison with other, especially Slavic, languages.

Social Sharing of Emotion During the COVID-19 Pandemic

The long-term uncertainty and persistence of isolation caused by the COVID-19 pandemic created prolonged emotional distress individually and collectively. As the pandemic progressed, the dynamic ride of emotional experience was expressed live and shared online, particularly on social media. In this study, we collected posted messages on Twitter for a longitudinal investigation to determine how emotional experiences changed over time during the pandemic. In total, we analyzed 41,868,013 COVID-19-related tweets in English posted from January 21 to July 31, 2020. Using a stage model, the results demonstrated that there were three stages during the pandemic characterized by distinct emotional changes. The first stage features high anxiety and negative emotions compared with the other stages, possibly due to the lack of information about the disease. The second stage shows the dynamic ride of all emotions and an increase in negative emotions (particularly anger) as the COVID-19 pandemic proceeds. In the third stage, most emotions are stabilized, except for depression, despite the protracted pandemic.

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model

In today's era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user's behavior. In this paper, these types of sentiments are classified to predict the mental illness of the user using the ensembled deep learning model. The Reddit social networking platform is used for the analysis, and the ensembling deep learning model is implemented through convolutional neural network and the recurrent neural network. In this work, multiclass classification is performed for predicting mental illness such as anxiety vs. nonanxiety, bipolar vs. nonbipolar, dementia vs. nondementia, and psychotic vs. nonpsychotic. The performance parameters used for evaluating the models are accuracy, precision, recall, and F1 score. The proposed ensemble model used for performing the multiclass classification has performed better than the other models, with an accuracy greater than 92% in predicting the class.

Beyond English: Considering Language and Culture in Psychological Text Analysis

The paper discusses the role of language and culture in the context of quantitative text analysis in psychological research. It reviews current automatic text analysis methods and approaches from the perspective of the unique challenges that can arise when going beyond the default English language. Special attention is paid to closed-vocabulary approaches and related methods (and Linguistic Inquiry and Word Count in particular), both from the perspective of cross-cultural research where the analytic process inherently consists of comparing phenomena across cultures and languages and the perspective of generalizability beyond the language and the cultural focus of the original investigation. We highlight the need for a more universal and flexible theoretical and methodological grounding of current research, which includes the linguistic, cultural, and situational specifics of communication, and we provide suggestions for procedures that can be implemented in future studies and facilitate psychological text analysis across languages and cultures.