Using topic modeling to infer the emotional state of people living with Parkinson’s disease

Valenti, Andrew; Chita-Tegmark, Meia; Tickle-Degnen, Otr Linda; Faota, L; Bock, Alexander; Scheutz, Matthias

doi:10.1080/10400435.2019.1623342

Cited by 13 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 3) Compared to LIWC, the topic features we proposed were more important and had incremental effects on the overall performance of models with different lag days, which indicated that mental health-related linguistic features were more targeted to OPHS behavior prediction. It could be supported by a previous study which found that the LIWC model performs better in the document with approximately 22 sentences while the topic model performs better in the document with about two sentences (52). The help-seeking posts are usually short and express their psychological problems, which implies that the topic model performs reasonably better.…”

Section: Principal Resultsmentioning

confidence: 56%

Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method

Hui

Zhang

Huang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Online mental health service (OMHS) has been named as the best psychological assistance measure during the COVID-19 pandemic. An interpretable, accurate, and early prediction for the demand of OMHS is crucial to local governments and organizations which need to allocate and make the decision in mental health resources. The present study aimed to investigate the influence of the COVID-19 pandemic on the online psychological help-seeking (OPHS) behavior in the OMHS, then propose a machine learning model to predict and interpret the OPHS number in advance. The data was crawled from two Chinese OMHS platforms. Linguistic inquiry and word count (LIWC), neural embedding-based topic modeling, and time series analysis were utilized to build time series feature sets with lagging one, three, seven, and 14 days. Correlation analysis was used to examine the impact of COVID-19 on OPHS behaviors across different OMHS platforms. Machine learning algorithms and Shapley additive explanation (SHAP) were used to build the prediction. The result showed that the massive growth of OPHS behavior during the COVID-19 pandemic was a common phenomenon. The predictive model based on random forest (RF) and feature sets containing temporal features of the OPHS number, mental health topics, LIWC, and COVID-19 cases achieved the best performance. Temporal features of the OPHS number showed the biggest positive and negative predictive power. The topic features had incremental effects on performance of the prediction across different lag days and were more suitable for OPHS prediction compared to the LIWC features. The interpretable model showed that the increase in the OPHS behaviors was impacted by the cumulative confirmed cases and cumulative deaths, while it was not sensitive in the new confirmed cases or new deaths. The present study was the first to predict the demand for OMHS using machine learning during the COVID-19 pandemic. This study suggests an interpretable machine learning method that can facilitate quick, early, and interpretable prediction of the OPHS behavior and to support the operational decision-making; it also demonstrated the power of utilizing the OMHS platforms as an always-on data source to obtain a high-resolution timeline and real-time prediction of the psychological response of the online public.

show abstract

Section: Principal Resultsmentioning

confidence: 56%

Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method

Hui

Zhang

Huang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Topic modelling has been used in the medical literature to identify syndromic features of clinical records [ 23 ], track disease prevalence in social media [ 39 , 40 ] and annotate human clinical notes datasets to evaluate potential genotype correlation with topics or to classify for ICD-10 annotation [ 24 , 25 ]. Here, for the first time we demonstrate its utility for rapid and unsupervised disease outbreak detection using the clinical narrative component of veterinary EHRs and show how simple deconstructions of the word foundations for several individual topics can provide transparent, clinician-friendly outputs to understand the significance of any signals.…”

Section: Discussionmentioning

confidence: 99%

“…Topic modelling is an unsupervised approach that assumes collections of documents can be categorised as having content with a probability of relating to specific topics where each topic is represented by the probability of any given word from the full corpus of text being present within that topic [ 19 ]. This method has been evaluated for identification of disease outbreaks in news media, clinical disease staging, medication prescribing patterns and adverse drug reaction classification [ 20 – 23 ] and in classifying EHRs for identification of phenotype and some clinical syndromes in human medicine [ 24 , 25 ]. Here, for the first time, we apply topic modelling to a large corpus of veterinary clinical narratives and evaluate whether it would have detected a gastroenteric disease outbreak using data retrospectively collected during this rare event.…”

Section: Introductionmentioning

confidence: 99%

Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs

et al. 2021

View full text Add to dashboard Cite

A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the ‘gastroenteric’ MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived ‘gastroenteric’ MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.

show abstract

“…This structure and the words to be included in each category are created from psychological concepts with the help of judges. LIWC is used to classify narcissism [50], emotion [51], fake news [52], and propaganda [26] from the textual content. As LIWC can be used with customized dictionaries, the translated version of dictionaries are developed for linguistic analysis in French [53], Spanish [54], Chinese [55], and Portuguese [56] languages.…”

Section: A Linguistic Inquiry and Word Count (Liwc)mentioning

confidence: 99%

ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

2020

View full text Add to dashboard Cite

Today, the rapid dissemination of information on digital platforms has seen the emergence of information pollution such as misinformation, disinformation, fake news, and different types of propaganda. Information pollution has become a serious threat to the online digital world and has posed several challenges to social media platforms and governments around the world. In this paper, we propose Propaganda Spotting in Online Urdu Language (ProSOUL)-a framework to identify content and sources of propaganda spread in the Urdu language. First, we develop a labelled dataset of 11,574 Urdu news to train the machine learning classifiers. Next, we develop the Linguistic Inquiry and Word Count (LIWC) dictionary to extract psycho-linguistic features of Urdu text. We evaluate the performance of different classifiers by varying n-gram, News Landscape (NELA), Word2Vec, and Bidirectional Encoder Representations from Transformers (BERT) features. Our results show that the combination of NELA, word n-gram, and character n-gram features outperform with 0.91 accuracy for Urdu text classification. In addition, Word2Vec embedding outperforms BERT features in classification of the Urdu text with 0.87 accuracy. Moreover, we develop and classify large scale Urdu content repositories to identify web sources spreading propaganda. Our results show that ProSOUL framework performs best for propaganda detection in the online Urdu news content compared to the general web content. To the best of our knowledge, this is the first study on the detection of propaganda content in the Urdu language.

show abstract

Using topic modeling to infer the emotional state of people living with Parkinson’s disease

Cited by 13 publications

References 11 publications

Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method

Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method

Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs

ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

Contact Info

Product

Resources

About