The first question is very broad, 95% accuracy doesn't say a lot, there are tasks where 99% is standard and there are tasks where an accuracy of 60% is amazing.
It is hard to answer this question without more information about your model, dataset and the affects of its outcome. Basically you should ask yourself if it is good enough for your task.
Regarding the second question, I would suggest to use a weighted loss function, where mistakes on the rarer classes have a higher price (it encourages the model to classify them correctly). For optimal performance, you should retrain your model from scratch.
Note for future reference, it is better to present the results in a confusion matrix, than just the final classification (gives more data, especially when we talk about a skewed dataset).