Table 13.
Tools and Open-source library of STTM
| Reference | Tools/open-source | Programming language | Inference/parameter | Source code reference | Build-in models |
|---|---|---|---|---|---|
|
Qiang et al. (2020) Qiang, Li, Yuan, Liu, et al. (2018a) |
STTM | Java | Gibbs sampling | https://github.com/qiang2100/STTM | LDA, DMM, LF-LDA, LF-DMM, GPU-PDMM, GPU-DMM, SATM, PTM, WNTM, and BTM |
| Nguyen (2018) | jLDADMM | Java | Collapsed Gibbs sampling | https://github.com/datquocnguyen/jLDADMM | LDA, DMM |
| Nguyen et al. (2015) | LFTM | Java | Gibbs sampling | https://github.com/datquocnguyen/LFTM | LF-LDA, LF-DMM |
| Zhao et al. (2011) | Twitter-LDA | Java | Gibbs sampling | https://github.com/minghui/Twitter-LDA | Twitter-LDA |
| Rubin et al. (2012) | DependencyLDA |
MATLAB (and C) |
https://github.com/timothyrubin/DependencyLDA | Dependency-LDA, Prior-LDA and Flat-LDA | |
| Mai et al. (2021) | TSSE-DMM | Python | https://github.com/PasaLab/TSSE | TSSE-DMM | |
| Li et al. (2021) | LapDMM | C ++ | https://github.com/li-ximing/LapDMM | LapDMM | |
| Yin and Wang (2014) | GSDMM | Python | Collapsed Gibbs Sampling | https://github.com/jackyin12/GSDMM | GSDMM |
| Huang et al. (2020) | NBTMWE | Java | Collapse Gibbs sSmpling | https://github.com/Jenny-HJJ/NBTMWE | NBTMWE |
| Cheng et al. (2014) | BTM | C ++ | Gibbs sampling | BTM, OBTM, IBTM, | |
| Yan et al. (2015) | BurstyBTM | C ++ | Gibbs sampling | https://github.com/xiaohuiyan/BurstyBTM | BurstyBTM |
| Gao et al. (2019) | CRFTM | java | Gibbs sampling | https://github.com/nonobody/CRFTM | CRFTM |
| Wang et al. (2018) | ASTM | java | ASTM | ||
| Li et al. (2016a) | GPUDMM | Java | Gibbs sampling | https://github.com/NobodyWHU/GPUDMM | - |
| Miao et al. (2016) | NVDM | Python | variational inference | https://github.com/ysmiao/nvdm | - |
| Srivastava and Sutton (2017) | ProdLDA | Python | https://github.com/akashgit/autoencoding_vi_for_topic_models | - | |
| Zhu et al. (2018) | GraphBTM | Python | Amortized variational inference | https://github.com/valdersoul/GraphBTM | - |
| Wu et al. (2020b) | NQTM | Python | https://github.com/BobXWu/NQTM | - | |
| Pham and Le (2020) | PLSV-VAE | Python | variational auto-encoder (VAE) inference | https://github.com/dangpnh2/plsv_vae | |
| Pham and Le (2021) | HTV | Python | https://github.com/dangpnh2/htv | ||
| Röder et al. (2015) | Project Palmetto | Java | Tools for evaluation TM | https://github.com/dice-group/Palmetto | Topic coherence |