Using LSTM for binary text Classification, getting almost same accuracy at each epoch
First of all, this is a binary classification problem (positive sentiment / negative sentiment), correct? And the dataset is roughly balanced? What were you trying to achieve by sorting them by length? Why are you using a generator and not just running fit on the dataset?

Don't worry about local minima. What you're looking at is a model/optimizer combination that converges to around 74% on this dataset. The one being fed sorted data has a higher accuracy at the start of the epoch because short tweets are easier to classify than long ones. And I wouldn't even worry about the batch-by-batch accuracy, just the epoch-by-epoch curve.