How to tackle a multilabel classification problem I am trying to build a LSTM model for a multiclass classification problem on textual data. Until now, I have only built a model when one input belongs to one of the categories. What do I do when one input can belong to more than one class (i.e.: one entry of data can belong to 2-3 categories)? Can anyone help me with some blogs or resources to build an intuition for making such a model?

Thanks.
LSTMs, like any other neural net, implicitly support multi-label classification.  You should ensure that your output layer has $n$ neurons, one for each class, and you should use logistic activation rather than softmax activation (which is typically used for the final layer in multi-class problems).
Each neuron $N_i$ in the final layer will output a value between 0 and 1.  If the output of $N_i$ is greater than 0.5, then the example is a member of label $i$.
Here's a pretty good article that might help.