Ouverture mediterraneene

Shapero, Harold; Milhaud, Darius

doi:10.2307/893589

Cited by 2 publications

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“…However, detecting emotions such as hopelessness in human text is inherently plagued by the flexibility of words such as “hope” and “wish”. Both 3 and 4 find a surprising role for structural features in real suicide notes—which are not obviously emotional in nature. A parallel in the current task is that the highest prevalence is instructions in the notes.…”

Section: Resultsmentioning

confidence: 99%

“…The distinction between content and structural features is emphasised in the work of Shapero 4 which describes an extensive investigation into the language used in suicide notes, and reports on the features found more commonly in genuine notes than simulated notes. Genuine notes are found to include affection, the future tense, references to family members, pronouns, names, negatives, intensifiers and maximum quantity terms.…”

Section: Related Workmentioning

confidence: 99%

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Three Hybrid Classifiers for the Detection of Emotions in Suicide Notes

Liakata

Kim

Saha

et al. 2012

Biomed Inform Insights

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We describe our approach for creating a system able to detect emotions in suicide notes. Motivated by the sparse and imbalanced data as well as the complex annotation scheme, we have considered three hybrid approaches for distinguishing between the different categories. Each of the three approaches combines machine learning with manually derived rules, where the latter target very sparse emotion categories. The first approach considers the task as single label multi-class classification, where an SVM and a CRF classifier are trained to recognise fifteen different categories and their results are combined. Our second approach trains individual binary classifiers (SVM and CRF) for each of the fifteen sentence categories and returns the union of the classifiers as the final result. Finally, our third approach is a combination of binary and multi-class classifiers (SVM and CRF) trained on different subsets of the training data. We considered a number of different feature configurations. All three systems were tested on 300 unseen messages. Our second system had the best performance of the three, yielding an F1 score of 45.6% and a Precision of 60.1% whereas our best Recall (43.6%) was obtained using the third system.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Three Hybrid Classifiers for the Detection of Emotions in Suicide Notes

Liakata

Kim

Saha

et al. 2012

Biomed Inform Insights

View full text Add to dashboard Cite

show abstract

“…Shapero6 collected the Birmingham Corpus of Suicide Notes, which contains 286 notes, 212 by males and 74 by females. This corpus also includes 33 genuine and 33 simulated suicide notes from Shneidman 7.…”

Section: Related Workmentioning

confidence: 99%

Rule-based and Lightly Supervised Methods to Predict Emotions in Suicide Notes

Pedersen

2012

Biomed Inform Insights

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This paper describes the Duluth systems that participated in the Sentiment Analysis track of the i2b2/VA/Cincinnati Children’s 2011 Challenge. The top Duluth system was a rule-based approach derived through manual corpus analysis and the use of measures of association to identify significant ngrams. This performed in the median range of systems, attaining an F-measure of 0.45. The second system was automatically derived from the most frequent bigrams unique to one or two emotions. It achieved an F-measure of 0.36. The third system was the union of the first two, and reached an F-measure of 0.44.

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Ouverture mediterraneene

Cited by 2 publications

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Three Hybrid Classifiers for the Detection of Emotions in Suicide Notes

Three Hybrid Classifiers for the Detection of Emotions in Suicide Notes

Rule-based and Lightly Supervised Methods to Predict Emotions in Suicide Notes

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