scholarly journals Political Alignment Identification: a Study with Documents of Argentinian Journalists

2020 ◽  
Vol 20 (1) ◽  
pp. e05
Author(s):  
Viviana Mercado ◽  
Andrea Villagra ◽  
Marcelo Errecalde
Keyword(s):  
Data Sets ◽  
Learning Approaches ◽  
Word Count ◽  
Political Bias ◽  
Linguistic Inquiry ◽  
Automatic Text Analysis ◽  
Author Profiling ◽  
Political Alignment ◽  
Automatic Text ◽  
Adequate Data

Political alignment identification is an author profiling task that aims at identifying political bias/orientation in people’ writings. As usual in any automatic text analysis, a critical aspect here is having available adequate data sets so that the data mining and machine learning approaches can obtain reliable and informative results. This article makes a contribution in this regard by presenting a new corpus for the study of political alignment in documents of Argentinian journalists. Thestudy also includes several kinds of analysis of documents of pro-government and opposition journalists such as the relevance of terms in each journalist class,sentiment analysis, topic modelling and the analysis of psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count (LIWC) system. From the experimental results, interesting patterns could be observed such as the topics both types of journalists write about, how the sentiment polarities are distributed and how the writings of pro-government and opposition journalists differ in the distinct LIWC categories.

scholarly journals Psychometric evaluation of the Serbian dictionary for automatic text analysis - LIWCser

Psihologija ◽  
2014 ◽  
Vol 47 (1) ◽  
pp. 5-32 ◽  
Author(s):  
Jovana Bjekic ◽  
Ljiljana Lazarevic ◽  
Marko Zivanovic ◽  
Goran Knezevic
Keyword(s):  
Text Analysis ◽  
Psychometric Evaluation ◽  
Scientific Paper ◽  
Personality Psychology ◽  
World Languages ◽  
Word Level ◽  
Different Types ◽  
Linguistic Inquiry ◽  
Automatic Text Analysis ◽  
Automatic Text

LIWC (Linguistic Inquiry and Word Count) is widely used word-level content analysis software. It was used in large number of studies in the fields of clinical, social and personality psychology, and it is adapted for text analysis in 11 world languages. The aim of this research was to validate empirically newly constructed adaptation of LIWC software for Serbian language (LIWCser). The sample of the texts consisted of 384 texts in Serbian and 141 texts in English. It included scientific paper abstracts, newspaper articles, movie subtitles, short stories and essays. Comparative analysis of Serbian and English version of the software demonstrated acceptable level of equivalence (ICCM=.70). Average coverage of the texts with LIWCser dictionary was 69.93%, and variability of this measure in different types of texts is in line with expected. Adaptation of LIWC software for Serbian opens entirely new possibilities of assessment of spontaneous verbal behaviour that is highly relevant for different fields of psychology.

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

Citation context-based topic models: discovering cited and citing topics from full text

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lixue Zou ◽  
Xiwen Liu ◽  
Wray Buntine ◽  
Yanli Liu
Keyword(s):  
Full Text ◽  
Topic Models ◽  
Content Type ◽  
Reference Information ◽  
Citation Context ◽  
Comparable Performance ◽  
Automatic Text Analysis ◽  
Collapsed Gibbs Sampling ◽  
Source Of Information ◽  
Automatic Text

PurposeFull text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms.Design/methodology/approachThe authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated.FindingsThe results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references.Originality/valueThe automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.

scholarly journals Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing

2020 ◽  
Author(s):  
Kashyap Chhatbar ◽  
Justyna Cholewa-Waclaw ◽  
Ruth Shah ◽  
Adrian Bird ◽  
Guido Sanguinetti
Keyword(s):  
Alternative Splicing ◽  
Dna Sequences ◽  
Molecular Mechanisms ◽  
Data Sets ◽  
Learning Approaches ◽  
Global Regulator ◽  
Major Function ◽  
Quantitative Analyses ◽  
Different Levels

AbstractMeCP2 is an abundant protein in mature nerve cells, where it binds to DNA sequences containing methylated cytosine. Mutations in the MECP2 gene cause the severe neurological disorder Rett syndrome (RTT), provoking intensive study of the underlying molecular mechanisms. Multiple functions have been proposed, one of which involves a regulatory role in splicing. Here we leverage the recent availability of high-quality transcriptomic data sets to probe quantitatively the potential influence of MeCP2 on alternative splicing. Using a variety of machine learning approaches that can capture both linear and non-linear associations, we show that widely different levels of MeCP2 have a minimal effect on alternative splicing in three different systems. Alternative splicing was also apparently indifferent to developmental changes in DNA methylation levels. Our results suggest that regulation of splicing is not a major function of MeCP2. They also highlight the importance of multi-variate quantitative analyses in the formulation of biological hypotheses.

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.

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