Table 4 Comparison of the performance of our gender-based classifiers with the BoP representation against state-of-the-art results. The results marked with an “*” correspond to approaches using both text and image features
From: A profile-based sentiment-aware approach for depression detection in social media
Dataset | Approach | F1 (positive class) |
|---|---|---|
Gender-based classifiers using BOP | 0.71 (±−0.02) | |
BCSGB model [30] | 0.64 | |
gSC model [54] | 0.59 | |
BoSE-ngrams [11] | 0.63 | |
DeepBoSE [55] | 0.65 | |
Macro-F1 | ||
Gender-based classifiers using BOP | 0.87 (± 0.01) | |
MDL [50] | 0.85* | |
DFC+FC [56] | 78.5 (± 1.2)* | |
GRU+VGG-NET+COMMA [57] | 0.90* | |
BiGRU-CNN [58] | 0.85 |