Data61-CSIRO systems at the CLPsych 2016 Shared Task

Kim, Sunghwan Mac; Wang, Yufei; Wan, Stephen; Paris, Cécile

doi:10.18653/v1/w16-0313

Cited by 19 publications

(15 citation statements)

References 4 publications

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“…As illustrated in Table (a), our models outperform the baseline and all top performing state of the art systems by large margins. We observe that the non‐ green macro average F1 score for the individual and ensemble models improves over the best system (Kim et al, ) by +12% and +17%, respectively. Similarly, we observe that the F1 scores for the flagged category is 3% and 5% higher than the best system with the individual and ensemble models, respectively.…”

Section: Results and Analysismentioning

confidence: 67%

“…Most of the systems, generally used Support Vector Machine (SVM) classifiers (Cortes & Vapnik, ) or an ensemble of some other standard classifiers for identifying the content severity. We briefly describe the top three approaches: Kim, Wang, Wan, and Paris () used a Stochastic Gradient Decent classification framework. They utilized the body of the text as the main source for feature extraction and represented the post by weighted TF‐IDF unigrams and distributed representation of documents (Le & Mikolov, ).…”

Section: Related Workmentioning

confidence: 99%

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Triaging content severity in online mental health forums

Cohan

Young

Yates

et al. 2017

Asso for Info Science & Tech

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Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential selfharm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.

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Section: Results and Analysismentioning

confidence: 67%

Section: Related Workmentioning

confidence: 99%

Triaging content severity in online mental health forums

Cohan

Young

Yates

et al. 2017

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…The three best performing teams obtained a macroaveraged fmeasure of 0.42, but used very different approaches to do so. Kim et al (2016) combined relatively few features (unigrams and post embeddings) with an ensemble of SGD classifiers. Brew (2016) used traditional n-gram features with a well-tuned SVM classifier, and achieved the best separation of urgent posts (crisis and red versus amber and green) with 0.69 f-measure.…”

Section: Online Forums and Support Groupsmentioning

confidence: 99%

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

224

148

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Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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“…Triaging mental health posts [164] Depression and PTSD detection [166] Adverse drug reaction mention detection [87] Adverse drug reaction extraction [87] Task type Shared task best: 0.42 [165] Shared task best: 0.84 [167] Shared task best: 0.42 [168] Upper limits: 0.54 (Twitter) 0.68 (DailyStrength) Shared task best: 0.61 [169] Upper limits: 0.72 (Twitter) 0.82 (DailyStrength)…”

Section: Taskmentioning

confidence: 99%

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Gonzalez-Hernandez¹,

Sarker²,

O’Connor³

et al. 2017

Yearb Med Inform

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SummaryBackground: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems.

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Data61-CSIRO systems at the CLPsych 2016 Shared Task

Cited by 19 publications

References 4 publications

Triaging content severity in online mental health forums

Triaging content severity in online mental health forums

Natural language processing in mental health applications using non-clinical texts

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

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