Sentiment Analysis of Suicide Notes: A Shared Task

Pestian, John; Matykiewicz, Paweł; Linn-Gust, Michelle; South, Brett R.; Uzuner, Özlem; Wiebe, Jan; Cohen, K. Bretonnel; Hurdle, John F.; Brew, Christopher

doi:10.4137/bii.s9042

Cited by 148 publications

(136 citation statements)

References 19 publications

(19 reference statements)

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“…age, gender, location). There are many At the text level, Pestian et al (2012) host a shared task for mining emotions from suicide notes. Nguyen et al (2014) separate out LiveJournal posts that discuss depression and related topics.…”

Section: Nlp In Mental Health Applications 651 2 Methods and Overviewmentioning

confidence: 99%

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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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Section: Nlp In Mental Health Applications 651 2 Methods and Overviewmentioning

confidence: 99%

“…Removing this reliance on expert annotation appears to be the focus of Pestian et al (2012), which presents a shared task for annotating suicide notes. Shared tasks are common computer science activities where research groups compete to solve the same problem.…”

Section: Suicide Notesmentioning

confidence: 99%

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

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

224

148

View full text Add to dashboard Cite

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“…The trec_eval evaluation tool 27 was used to calculate these evaluation measures 28 . Participants were also provided with other standard measures calculated by trec_eval 29 .…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…27 http://trec.nist.gov/trec_eval/ 28 NDCG was computed with the standard settings in trec_eval, and by running the command trec_eval -c -M1000 -m ndcg_cut qrels runName 29 Including P@5, NDCG@5, Mean Average Precision (MAP), and rel_ret (i.e., the total number of relevant documents retrieved by the system over all queries).…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…Data contained around 350,000 abstracts from the MED-LINE database over five years, manually created topics, and a topic set based on the standardised MeSH. 10 The Genomics Track [23] [28] and 2011 [29] addressed automated diagnosis coding of radiology reports and classifying the emotions found in suicide notes. In 2007, 1,954 de-identified radiology reports in English from a US radiology department for children were used.…”

Section: Introductionmentioning

confidence: 99%

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Overview of the ShARe/CLEF eHealth Evaluation Lab 2013

Suominen

Salanterä

Velupillai

et al. 2013

Lecture Notes in Computer Science

185

171

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Abstract. Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition. This ShARe/CLEFeHealth2013 lab offered student mentoring and shared tasks: identification and normalisation of disorders (1a and 1b) and normalisation of abbreviations and acronyms (2) Overview of the ShARe/CLEF eHealth Evaluation Lab 2013 213 reports with respect to terminology standards in healthcare as well as information retrieval (3) to address questions patients may have when reading clinical reports. The focus on patients' information needs as opposed to the specialised information needs of physicians and other healthcare workers was the main feature of the lab distinguishing it from previous shared tasks. De-identified clinical reports for the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Task 3 were from the Internet and originated from the Khresmoi project. Task 1 annotations originated from the ShARe annotations. For Tasks 2 and 3, new annotations, queries, and relevance assessments were created. 64, 56, and 55 people registered their interest in Tasks 1, 2, and 3, respectively. 34 unique teams (3 members per team on average) participated with 22, 17, 5, and 9 teams in Tasks 1a, 1b, 2 and 3, respectively. The teams were from Australia, China, France, India, Ireland, Republic of Korea, Spain, UK, and USA. Some teams developed and used additional annotations, but this strategy contributed to the system performance only in Task 2. The best systems had the F1 score of 0.75 in Task 1a; Accuracies of 0.59 and 0.72 in Tasks 1b and 2; and Precision at 10 of 0.52 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development.

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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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Sentiment Analysis of Suicide Notes: A Shared Task

Cited by 148 publications

References 19 publications

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

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

Overview of the ShARe/CLEF eHealth Evaluation Lab 2013

Triaging content severity in online mental health forums

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