The data processing in this repo has mostly been done in R, with training of ABSA models done in python. The respective source files can be found in the code folder. They are numbered for the order in which they have been run. The code folder also contains the annotated data set created for this thesis, as pyABSA has a strange way of handling paths, so input and output files of code/04-train.ipynb were just left in that directory, except for the trained models, which are in mv_checkpoints.
The file python-env-personalities.yml contains the installed packages in the conda environment used for this project.
Consult the session info printout below for version of used packages:
sessionInfo()
#> R version 4.2.3 (2023-03-15)
#> Platform: x86_64-redhat-linux-gnu (64-bit)
#> Running under: Fedora Linux 37 (Workstation Edition)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/libflexiblas.so.3.3
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> loaded via a namespace (and not attached):
#> [1] digest_0.6.31 withr_2.5.0 R.methodsS3_1.8.2 lifecycle_1.0.3
#> [5] magrittr_2.0.3 reprex_2.0.2 evaluate_0.20 rlang_1.1.0
#> [9] cli_3.6.0 rstudioapi_0.14 fs_1.6.1 R.utils_2.12.2
#> [13] R.oo_1.25.0 vctrs_0.6.0 styler_1.9.1 rmarkdown_2.20
#> [17] tools_4.2.3 R.cache_0.16.0 glue_1.6.2 purrr_1.0.1
#> [21] xfun_0.37 yaml_2.3.7 fastmap_1.1.1 compiler_4.2.3
#> [25] htmltools_0.5.4 knitr_1.42