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I have a Pandas DataFrame which consists of a body of text in 1 row. Each body of text is assigned labels for Category (in 1 row) and a Topic (in a separate row). Example of DataFrame below:

Text Category Topic
cognitive training ... Category A Topic 2
correlation between ... Category F Topic 56

There are 8 Categories with a total of 60 unique Topics. These Topics are not equally shared amongst the Categories, for example Category A can have Topics 1-18 and Category B can have Topics 19-27. A basic diagram has been added below demonstrating the levels.

                     <ROOT>
            ____________|______________________
           /            |        \          \ ...
        Cat A         Cat B      Cat C
      / / | \ \        |  \        | 
     1 2  3  4 5 ...  19  20 ...   28

I would like to know the best approach(s) for generating a Hierarchical Classification where both the Category and Topic are generated in the prediction. So if a new body of text is received both levels can be predicted e.g. Category and Topic.

I have been using TfIdf on the body of text and how next to proceed is troubling me.

from sklearn_hierarchical_classification.classifier import HierarchicalClassifier

Attempting to use this library isnt providing me with both levels as a prediction.

Any help or ideas in implementing such a model would be really appreciated :) Many thanks!

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  • $\begingroup$ In other words, do you want to classify according to the topic's meaning? For instance: "insect" or "bug" goes to "Insect category" and "keyboard" or "computer bug" go to "IT category"? $\endgroup$
    – Nicolas Martin
    Commented Oct 27, 2022 at 17:59
  • $\begingroup$ Hi @NicolasMartin , thanks for your reply. I would like to get both levels of the classification and see the path. So I want to see both the Category and Topic classified. If classifying animals for example and the model predicts "snake" I also want to see the level above that "reptile". To ensure the upper level is correct as well. Does that make sense? $\endgroup$
    – user142080
    Commented Oct 28, 2022 at 7:05

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Bert is a model that is able to extract meaning from text and make good classifications, even if the text hasn't been learned before.

That's why you can train a Bert model with many sentences that cover all classes, and then you should be able to classify any text.

https://www.analyticsvidhya.com/blog/2021/12/text-classification-using-bert-and-tensorflow/

https://www.kaggle.com/code/merishnasuwal/document-classification-using-bert

This is the most universal solution adapted to any text.

Here are all text classification models:

https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads

They are mostly built to classify sentiment, but they could classify anything, like this one:

https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-multi-all?text=I%27m+sure+the+%7B%40Tampa+Bay+Lightning%40%7D+would%E2%80%99ve+rather+faced+the+Flyers+but+man+does+their+experience+versus+the+Blue+Jackets+this+year+and+last+help+them+a+lot+versus+this+Islanders+team.+Another+meat+grinder+upcoming+for+the+good+guys

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