I'm not sure there is necessarily any problem. If you are already fine-tuning a pre-trained language model (e.g. ULMFiT, BERT or something similar), then how much you gain through augmentation is a bit variable from application to application. It may have been reasonable to hope for a bit more, but it may no add that much. If you look at this paper (also an example of sentiment classification), where round-trip translation was used as an augmentation, the gains from augmentation are huge over just fine-tuning a pre-trained model when you have very little data (e.g. 50 examples). However, they get a lot smaller (order of magnitude similar to what you see Figure 1), when the number of examples is larger. I looked at both the 5000 records case (similar number of records) and the 500 records case (similar error rate to yours + the smallest class is about similar sized to your case). I'm not exactly sure how I expect round-trip translation as an augmentation to work vs. what you did, but I'd speculate it might even be more effective (by the way, that type of augmentation is now super-easy, because huggingface offers translation models, so you could easily give it a try).
To check whether there's an issue: Did you inspect some examples to see that the augmentations really modify a lot of text without changing the meaning? Did you have a look at cases from your validation set that get mis-classified (Are they cases that are simply ambigious and no model can realistically get them right? Are the labels even wrong? etc.)?
With so few examples, it's realistic to try counter-factual augmentation of your training data. I.e. create (=a human does this manually) 2 or more versions of each training example that make the smallest possible change to the text that changes (in the judgement of the human) the category to the other 2 categories. It might be enough to do this for the training data cases that are particularly hard (e.g. using something like the BatchBALD criterion).
You could try test-time-augmentation like in the paper I linked.