r/MachineLearning - [D] Text classification on a small dataset

I am trying to perform multiclass text classification (for 24 classes) on a set documents, but I have a very small dataset currently (1200 total examples). The data collection process is a bit tedious in my case, hence the small dataset size. The best result I have achieved till now is 58% accuracy with an SVM model and a single layer CNN model. Is there any other approach I can try other than collecting more data? I have tried oversampling the training set, but it didn't seem to improve the performance.