Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes

Cherry, Colin; Mohammad, Saif M.; Bruijn, Berry de

doi:10.4137/bii.s8933

Cited by 43 publications

(41 citation statements)

References 24 publications

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“…Since the other teams (Cherry, Mohammad and De Bruijn 2012;Luyckx et al 2012;McCart et al 2012;Spasić et al 2012) reported details of their algorithms and outcomes, the shared task also provides insights into the techniques that did not produce satisfactory results. For example, Yu et al (2012) reported low accuracy using Wordnet, character n-grams and word n-grams (a contiguous sequence of n terms).…”

Section: Evaluating Labeling Systemsmentioning

confidence: 99%

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

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

237

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: Evaluating Labeling Systemsmentioning

confidence: 99%

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

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

237

148

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“…On the other hand, we are working on a project that aims to prevent suicide using social networks (Facebook, Twitter, forums, etc.). Cases of suicides have been reported in recent years as people have posted on social networks expressing their thought or addressing messages to their families (Cherry et al, 2012). We believe that sentiment and emotion analysis can be adapted to detect dysphoric states.…”

Section: Resultsmentioning

confidence: 99%

FEEL: a French Expanded Emotion Lexicon

Abdaoui

Azé

Bringay

et al. 2016

Lang Resources & Evaluation

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Abstract. Sentiment analysis allows the semantic evaluation of a piece of text according to the expressed sentiments and opinions. While considerable attention has been given to the polarity (positive, negative) of English words, only few studies were interested in the conveyed emotions (joy, anger, surprise, sadness, etc.) especially in other languages. In this paper, we present the elaboration and the evaluation of a new French lexicon considering both polarity and emotion. The elaboration method is based on the semi-automatic translation and expansion to synonyms of the English NRC Word Emotion Association Lexicon (NRCEmoLex). First, online translators have been automatically queried in order to create a first version of our new French Expanded Emotion Lexicon (FEEL). Then, a human professional translator manually validated the automatically obtained entries and the associated emotions. She agreed with more than 94% of the prevalidated entries (those found by a majority of translators) and less than 18% of the remaining entries (those found by very few translators). This result highlights that online tools can be used to get high quality resources with low cost. Annotating a subset of terms by three different annotators shows that the associated sentiments and emotions are consistent. Finally, extensive experiments have been conducted to compare the final version of FEEL with other existing French lexicons. Various French benchmarks for polarity and emotion classifications have been used in these evaluations. Experiments have shown that FEEL obtains competitive results for polarity, and significantly better results for basic emotions.

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“…Four machine learning algorithms were found to perform well in this problem space: support vector machines (SVM) (Alm et al, 2005;Aman & Szpakowicz, 2007;Brooks et al, 2013;Cherry et al, 2012), Bayesian networks (Sohn et al, 2012;Strapparava & Mihalcea, 2008), decision trees , and k-nearest neighbor (KNN) Holzman & Pottenger, 2003). The features were held constant across different classifiers in the candidate set.…”

Section: Machine Learning Experimentsmentioning

confidence: 99%

Proceedings of the NAACL Student Research Workshop

Andreas

Choi

Lazaridou

2016

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ii IntroductionWelcome to the NAACL-HLT 2016 Student Research Workshop.This year, we have three different kinds of papers: research papers, thesis proposals, and undergraduate research papers. Thesis proposals were intended for advanced students who have decided on a thesis topic and wish to get feedback on their proposal and broader ideas for their continuing work, while research papers describe completed work or work in progress with preliminary results. In order to encourage undergraduate research, we offered a special track for research papers where the first author is an undergraduate student.We received 11 research papers, 5 thesis proposals, and 8 undergraduate research papers -making the total number of submissions 24. We accepted 9 research papers, 2 thesis proposals, and 6 undergraduate research papers (17 accepted in total). This translates to an acceptance rate of 81% for research papers, 40% for thesis proposals, and 75% for undergraduate research papers (70% overall). This year, all the SRW papers will be presented at the main conference evening poster session. In addition, each SRW paper is assigned a dedicated mentor. The mentor is an experienced researcher from academia or industry who will prepare in-depth comments and questions in advance for the poster session and will provide feedback to the student author. Thanks to our funding sources, this year's SRW covers registration expenses and provides partial travel and/or lodging support to all student first authors of the SRW papers. We gratefully acknowledge the support from the NSF and Google. We thank our dedicated program committee members who gave constructive and detailed feedback for the student papers. We also would like to thank the NAACL-HLT 2016 organizers and local arrangement chairs. AbstractWe present an end-to-end method for learning verb-specific semantic frames with feedforward neural network (FNN). Previous works in this area mainly adopt a multi-step procedure including part-of-speech tagging, dependency parsing and so on. On the contrary, our method uses a FNN model that maps verbspecific sentences directly to semantic frames. The simple model gets good results on annotated data and has a good generalization ability. Finally we get 0.82 F-score on 63 verbs and 0.73 F-score on 407 verbs.

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Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes

Cited by 43 publications

References 24 publications

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

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

FEEL: a French Expanded Emotion Lexicon

Proceedings of the NAACL Student Research Workshop

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