Re-tagging customer opinions and identifying improvement areas

Hello KNIMErs,

I would need your suggestions for the subject I have never worked with.

I have access to raw data with clients’ opinions and to the platform that turns those opinions into sentiment analysis, tags, tag cloud, and the dashboard. Sentiment analysis looks OK (ratings are in line with the sentiment), however I have some doubts regarding tagging.

The key raw data are:

*

OPINION descriptive clients’ feedback. A couple of languages are present here.

*

RATING of 1, 2, 3, 4 or 5 where 1 stands for the lowest rate and 5 stands for the highest one.

*

SENTIMENT with positive, neutral, or negative values assigned.

*

TAG # - multiple columns dedicated for tags. The thing here is that there are non-blank opinions with no tags assigned.

Other data refers to products, distribution channels, clients’ details, etc.

Unfortunately, I can’t share sample dataset and – therefore - I can’t use public AI platforms while building the workflow.

I would need your suggestions on what the logic/sequence of nodes should be used to achieve two main goals:

1.

Re-assign tags based on OPINION.

1.

Identify areas requiring improvements.

Then, I could start building my tool based on your suggestions.

Any support is more then welcome.

PS. I have already played with the workflow available here Importing, preprocessing and visualizing textual data as Tag Cloud – KNIME Community Hub and my own dataset.