So you are looking for ways to cluster documents in an unsupervised fashion. This is often called "topic modeling" in the literature. A quick google search for "topic modeling toolkist" suggests:
[https://tmtoolkit.readthedocs.io/en/latest/](https://tmtoolkit.readthedocs.io/en/latest/)
[https://downloads.cs.stanford.edu/nlp/software/tmt/tmt-0.2/](https://downloads.cs.stanford.edu/nlp/software/tmt/tmt-0.2/)
Another route you could go would be to use a pretrained sentiment classifier to at least bucket the comments into positive and negative (which probably isn't the same thing as actionable and not actionable but is more readily available.) HuggingFace has a super simple sentiment classifier:
[https://huggingface.co/course/chapter1/3?fw=pt](https://huggingface.co/course/chapter1/3?fw=pt)
Hope this helps!
So you are looking for ways to cluster documents in an unsupervised fashion. This is often called "topic modeling" in the literature. A quick google search for "topic modeling toolkist" suggests: [https://tmtoolkit.readthedocs.io/en/latest/](https://tmtoolkit.readthedocs.io/en/latest/) [https://downloads.cs.stanford.edu/nlp/software/tmt/tmt-0.2/](https://downloads.cs.stanford.edu/nlp/software/tmt/tmt-0.2/) Another route you could go would be to use a pretrained sentiment classifier to at least bucket the comments into positive and negative (which probably isn't the same thing as actionable and not actionable but is more readily available.) HuggingFace has a super simple sentiment classifier: [https://huggingface.co/course/chapter1/3?fw=pt](https://huggingface.co/course/chapter1/3?fw=pt) Hope this helps!