practical improvements worth trying over plain LSTM in text classification?
A properly written LSTM network is quite powerful for NLP - why do you think that "combining" this with something else is the answer???

The project you're describing should be pretty easy to implement. The only barrier you have here is how much labeled data you can collect. Personally, I would focus on getting hundreds of thousands of examples, in a balanced dataset, and then feed that (with NLP approaches) into an LSTM network. You should be able to generate very good accuracy rates in a reasonable amount of time.