Topic moddeling, sentiment and correlation calculation (word2vec alternativ)

Hello, I am still quite new to the Knime context and would appreciate any help.

I am currently analyzing customer reviews and want to extract the respective service category (one or more) being reviewed and the associated sentiment.

Example review:

“The reception at the hotel unfortunately didn’t help us with our problem and persuaded us to take a day trip that was just planned as a sales event.

The organization with the bus transfer to the hotel worked out perfectly.”

My goal is to get a JSON output with the service categories (Topics) and their respective sentiment:

Transfer: positive
Reception: negative
Excursion: negative

A total of 50,000 reviews need to be analyzed.

Next, I want to perform a correlation calculation to determine which negative experiences most frequently lead to low-star ratings.

I initially wanted to work with word2vec, but it seems to have become legacy. Is there a newer method to replace it? I haven’t been able to find anything.

In general, when something becomes legacy, is there a place where one can find guidance or suggestions for current alternatives? And where can these be found?

I also discovered a workflow for my use case that uses GPT4All. However, my company has not approved the use of LLMs because the reviews contain personal data. Our data protection team is currently evaluating whether local LLMs can be approved, and I need to wait for their decision.

Can you recommend a workflow for my goals?