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depressionemo dataset  (Reddit Inc)

 
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    Structured Review

    Reddit Inc depressionemo dataset
    Class distribution of <t>DepressionEmo</t> and MDSD dataset
    Depressionemo Dataset, supplied by Reddit Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/depressionemo dataset/product/Reddit Inc
    Average 86 stars, based on 1 article reviews
    depressionemo dataset - by Bioz Stars, 2026-06
    86/100 stars

    Images

    1) Product Images from "Ensemble transformer with post-hoc explanations for depression emotion and severity detection"

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    Journal: iScience

    doi: 10.1016/j.isci.2025.114605

    Class distribution of DepressionEmo and MDSD dataset
    Figure Legend Snippet: Class distribution of DepressionEmo and MDSD dataset

    Techniques Used:

    Pearson correlation between emotion labels in the multi-label DepressionEmo dataset Warmer colors indicate higher co-occurrence across posts (e.g., worthlessness-hopelessness and loneliness-emptiness co-occur frequently), while Anger shows weaker correlations with inward-facing emotions.
    Figure Legend Snippet: Pearson correlation between emotion labels in the multi-label DepressionEmo dataset Warmer colors indicate higher co-occurrence across posts (e.g., worthlessness-hopelessness and loneliness-emptiness co-occur frequently), while Anger shows weaker correlations with inward-facing emotions.

    Techniques Used:

    Wordcloud representation of the DepressionEmo dataset after performing preprocessing steps
    Figure Legend Snippet: Wordcloud representation of the DepressionEmo dataset after performing preprocessing steps

    Techniques Used:

    Micro- and macro-averaged precision, recall, and F1-score comparison across all models on DepressionEmo and DSL (A, C, and E) show Precision, Recall, and F1 scores for DepressionEmo, while panels (B, D, and F) display the same metrics for DSL. The bars represent mean test performance (blue for micro, orange for macro), and the models are ranked by macro-F1 for each dataset. The proposed DepTformer-XAI-SV is marked with hatched bars.
    Figure Legend Snippet: Micro- and macro-averaged precision, recall, and F1-score comparison across all models on DepressionEmo and DSL (A, C, and E) show Precision, Recall, and F1 scores for DepressionEmo, while panels (B, D, and F) display the same metrics for DSL. The bars represent mean test performance (blue for micro, orange for macro), and the models are ranked by macro-F1 for each dataset. The proposed DepTformer-XAI-SV is marked with hatched bars.

    Techniques Used: Comparison

    Comparison of model performance across datasets using macro-averaged F1 scores (A) and (B) present the generalization trend of classifiers trained on the DepressionEmo and evaluated on the DSL dataset. Each line corresponds to a model family—transformers (blue), deep learning (green), and classical machine learning (gray)—with the proposed DepTformer-XAI-SV model (orange) highlighted. Upward slopes indicate improved generalization to DSL, whereas flatter or downward trends reflect limited transferability across datasets.
    Figure Legend Snippet: Comparison of model performance across datasets using macro-averaged F1 scores (A) and (B) present the generalization trend of classifiers trained on the DepressionEmo and evaluated on the DSL dataset. Each line corresponds to a model family—transformers (blue), deep learning (green), and classical machine learning (gray)—with the proposed DepTformer-XAI-SV model (orange) highlighted. Upward slopes indicate improved generalization to DSL, whereas flatter or downward trends reflect limited transferability across datasets.

    Techniques Used: Comparison

    Ablation and imbalance influence landscape across backbone ensembles and data-level interventions (A) Ablation robustness map: absolute change ( Δ ) in macro-F1 versus the full fusion, showing how each training constraint impacts both emotion (circle markers) and severity (triangle markers) encoders. Bars indicate absolute Δ macro-F1; marker color encodes minority-macro-F1, and whiskers denote 95% confidence intervals. Fusion- and threshold-level ablations (orange region) cause larger stability loss than data-level ones (blue region). (B) Backbone influence landscape (LOBO analysis): absolute macro-F1 loss when each backbone is removed from the ensemble. Blue (DepressionEmo) and orange (DSL) bars reflect distinct minority sensitivities. The right inset shows the trade-off correlation ( Δ macro-F1 vs. minority-F1), where stronger ensembles retain minority balance. (C) Imbalance sensitivity landscape: macro-averaged metrics under progressive imbalance corrections. Solid lines (DepressionEmo) and dashed lines (DSL) show F1, precision, and recall trends under class-weighting and oversampling caps. The shaded region marks DSL non-applicability. The inset (bottom-right) traces precision-recall trade-offs across oversampling ratios, revealing recall-driven F1 gains beyond r = 0.25 .
    Figure Legend Snippet: Ablation and imbalance influence landscape across backbone ensembles and data-level interventions (A) Ablation robustness map: absolute change ( Δ ) in macro-F1 versus the full fusion, showing how each training constraint impacts both emotion (circle markers) and severity (triangle markers) encoders. Bars indicate absolute Δ macro-F1; marker color encodes minority-macro-F1, and whiskers denote 95% confidence intervals. Fusion- and threshold-level ablations (orange region) cause larger stability loss than data-level ones (blue region). (B) Backbone influence landscape (LOBO analysis): absolute macro-F1 loss when each backbone is removed from the ensemble. Blue (DepressionEmo) and orange (DSL) bars reflect distinct minority sensitivities. The right inset shows the trade-off correlation ( Δ macro-F1 vs. minority-F1), where stronger ensembles retain minority balance. (C) Imbalance sensitivity landscape: macro-averaged metrics under progressive imbalance corrections. Solid lines (DepressionEmo) and dashed lines (DSL) show F1, precision, and recall trends under class-weighting and oversampling caps. The shaded region marks DSL non-applicability. The inset (bottom-right) traces precision-recall trade-offs across oversampling ratios, revealing recall-driven F1 gains beyond r = 0.25 .

    Techniques Used: Marker

    Learning curves of the proposed model on DepressionEmo and DSL (A) DepressionEmo and (B) DSL report epoch-wise trends for training and validation loss, accuracy, recall, and precision, illustrating stable convergence and consistent generalization across datasets.
    Figure Legend Snippet: Learning curves of the proposed model on DepressionEmo and DSL (A) DepressionEmo and (B) DSL report epoch-wise trends for training and validation loss, accuracy, recall, and precision, illustrating stable convergence and consistent generalization across datasets.

    Techniques Used: Biomarker Discovery



    Similar Products

    depressionemo dataset  (Reddit Inc)
    86
    Reddit Inc depressionemo dataset
    Class distribution of <t>DepressionEmo</t> and MDSD dataset
    Depressionemo Dataset, supplied by Reddit Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/depressionemo dataset/product/Reddit Inc
    Average 86 stars, based on 1 article reviews
    depressionemo dataset - by Bioz Stars, 2026-06
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    Class distribution of DepressionEmo and MDSD dataset

    Journal: iScience

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    doi: 10.1016/j.isci.2025.114605

    Figure Lengend Snippet: Class distribution of DepressionEmo and MDSD dataset

    Article Snippet: DepressionEmo dataset consists of 6,037 Reddit posts that represent a wide range of emotions associated with depression.

    Techniques:

    Pearson correlation between emotion labels in the multi-label DepressionEmo dataset Warmer colors indicate higher co-occurrence across posts (e.g., worthlessness-hopelessness and loneliness-emptiness co-occur frequently), while Anger shows weaker correlations with inward-facing emotions.

    Journal: iScience

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    doi: 10.1016/j.isci.2025.114605

    Figure Lengend Snippet: Pearson correlation between emotion labels in the multi-label DepressionEmo dataset Warmer colors indicate higher co-occurrence across posts (e.g., worthlessness-hopelessness and loneliness-emptiness co-occur frequently), while Anger shows weaker correlations with inward-facing emotions.

    Article Snippet: DepressionEmo dataset consists of 6,037 Reddit posts that represent a wide range of emotions associated with depression.

    Techniques:

    Wordcloud representation of the DepressionEmo dataset after performing preprocessing steps

    Journal: iScience

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    doi: 10.1016/j.isci.2025.114605

    Figure Lengend Snippet: Wordcloud representation of the DepressionEmo dataset after performing preprocessing steps

    Article Snippet: DepressionEmo dataset consists of 6,037 Reddit posts that represent a wide range of emotions associated with depression.

    Techniques:

    Micro- and macro-averaged precision, recall, and F1-score comparison across all models on DepressionEmo and DSL (A, C, and E) show Precision, Recall, and F1 scores for DepressionEmo, while panels (B, D, and F) display the same metrics for DSL. The bars represent mean test performance (blue for micro, orange for macro), and the models are ranked by macro-F1 for each dataset. The proposed DepTformer-XAI-SV is marked with hatched bars.

    Journal: iScience

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    doi: 10.1016/j.isci.2025.114605

    Figure Lengend Snippet: Micro- and macro-averaged precision, recall, and F1-score comparison across all models on DepressionEmo and DSL (A, C, and E) show Precision, Recall, and F1 scores for DepressionEmo, while panels (B, D, and F) display the same metrics for DSL. The bars represent mean test performance (blue for micro, orange for macro), and the models are ranked by macro-F1 for each dataset. The proposed DepTformer-XAI-SV is marked with hatched bars.

    Article Snippet: DepressionEmo dataset consists of 6,037 Reddit posts that represent a wide range of emotions associated with depression.

    Techniques: Comparison

    Comparison of model performance across datasets using macro-averaged F1 scores (A) and (B) present the generalization trend of classifiers trained on the DepressionEmo and evaluated on the DSL dataset. Each line corresponds to a model family—transformers (blue), deep learning (green), and classical machine learning (gray)—with the proposed DepTformer-XAI-SV model (orange) highlighted. Upward slopes indicate improved generalization to DSL, whereas flatter or downward trends reflect limited transferability across datasets.

    Journal: iScience

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    doi: 10.1016/j.isci.2025.114605

    Figure Lengend Snippet: Comparison of model performance across datasets using macro-averaged F1 scores (A) and (B) present the generalization trend of classifiers trained on the DepressionEmo and evaluated on the DSL dataset. Each line corresponds to a model family—transformers (blue), deep learning (green), and classical machine learning (gray)—with the proposed DepTformer-XAI-SV model (orange) highlighted. Upward slopes indicate improved generalization to DSL, whereas flatter or downward trends reflect limited transferability across datasets.

    Article Snippet: DepressionEmo dataset consists of 6,037 Reddit posts that represent a wide range of emotions associated with depression.

    Techniques: Comparison

    Ablation and imbalance influence landscape across backbone ensembles and data-level interventions (A) Ablation robustness map: absolute change ( Δ ) in macro-F1 versus the full fusion, showing how each training constraint impacts both emotion (circle markers) and severity (triangle markers) encoders. Bars indicate absolute Δ macro-F1; marker color encodes minority-macro-F1, and whiskers denote 95% confidence intervals. Fusion- and threshold-level ablations (orange region) cause larger stability loss than data-level ones (blue region). (B) Backbone influence landscape (LOBO analysis): absolute macro-F1 loss when each backbone is removed from the ensemble. Blue (DepressionEmo) and orange (DSL) bars reflect distinct minority sensitivities. The right inset shows the trade-off correlation ( Δ macro-F1 vs. minority-F1), where stronger ensembles retain minority balance. (C) Imbalance sensitivity landscape: macro-averaged metrics under progressive imbalance corrections. Solid lines (DepressionEmo) and dashed lines (DSL) show F1, precision, and recall trends under class-weighting and oversampling caps. The shaded region marks DSL non-applicability. The inset (bottom-right) traces precision-recall trade-offs across oversampling ratios, revealing recall-driven F1 gains beyond r = 0.25 .

    Journal: iScience

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    doi: 10.1016/j.isci.2025.114605

    Figure Lengend Snippet: Ablation and imbalance influence landscape across backbone ensembles and data-level interventions (A) Ablation robustness map: absolute change ( Δ ) in macro-F1 versus the full fusion, showing how each training constraint impacts both emotion (circle markers) and severity (triangle markers) encoders. Bars indicate absolute Δ macro-F1; marker color encodes minority-macro-F1, and whiskers denote 95% confidence intervals. Fusion- and threshold-level ablations (orange region) cause larger stability loss than data-level ones (blue region). (B) Backbone influence landscape (LOBO analysis): absolute macro-F1 loss when each backbone is removed from the ensemble. Blue (DepressionEmo) and orange (DSL) bars reflect distinct minority sensitivities. The right inset shows the trade-off correlation ( Δ macro-F1 vs. minority-F1), where stronger ensembles retain minority balance. (C) Imbalance sensitivity landscape: macro-averaged metrics under progressive imbalance corrections. Solid lines (DepressionEmo) and dashed lines (DSL) show F1, precision, and recall trends under class-weighting and oversampling caps. The shaded region marks DSL non-applicability. The inset (bottom-right) traces precision-recall trade-offs across oversampling ratios, revealing recall-driven F1 gains beyond r = 0.25 .

    Article Snippet: DepressionEmo dataset consists of 6,037 Reddit posts that represent a wide range of emotions associated with depression.

    Techniques: Marker

    Learning curves of the proposed model on DepressionEmo and DSL (A) DepressionEmo and (B) DSL report epoch-wise trends for training and validation loss, accuracy, recall, and precision, illustrating stable convergence and consistent generalization across datasets.

    Journal: iScience

    Article Title: Ensemble transformer with post-hoc explanations for depression emotion and severity detection

    doi: 10.1016/j.isci.2025.114605

    Figure Lengend Snippet: Learning curves of the proposed model on DepressionEmo and DSL (A) DepressionEmo and (B) DSL report epoch-wise trends for training and validation loss, accuracy, recall, and precision, illustrating stable convergence and consistent generalization across datasets.

    Article Snippet: DepressionEmo dataset consists of 6,037 Reddit posts that represent a wide range of emotions associated with depression.

    Techniques: Biomarker Discovery