Machine Learning Multi-ouput Classification

Hello Guys,

I am facing some issues with a text classification problem and need your help to understand the best way to approach it.

The problem statement is as follows:

Assume I have a set of sentences describing a certain product (assume they are furniture items). Each of the sentences needs to be tagged with a class coming from a roster of 10 possible classes. The 10 classes refer to possible attributes of the item, such as softness, brightness etc.

On top of this, sentences contain sentiment information on each item (good, bad, neutral). Example: "I am satisfied with the performance of this couch, it is really soft".

The first question is: how would you approach the problem of labeling each sentence with both classes AND sentiment?

Now, to make things even more complex, assume each sentence can describe up to 3 items, with relative sentiment.

How would you approach the multi-ouput sentiment+class problem in this case?


Thanks in advance for any help!


my first approach would be to train two classification models. One to predict sentiment labels and the second to predict the attributes. Do you have labeled data?

Cheers, Kilian

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