I have some experience with images and have played around with image classification using CNN's but have limited knowledge when it comes to text data.
The input that I currently want to classify is written as:
hjkhghkgfghjkhghkgfghfefdefdcdefghjkjh-hjhgfe fdcd-dd-fdc-dad-ad-dfe-cde-dggf-ghd-gg-bcd hjkhghkgfghjkhghkgfghfefdefdcdefghjkjh-gh-gfed dh-hg-gf-gh-dh-hg-gf-gh-hkhg-kh-hg-gf-gh-hkhg-kh-hg-gf-ghh-hgfg-dfd-dc-fgf-gh
I have over 2000 rows of this data, that needs to be classified. I know that for regular text data RNN networks and LSTM cells have been known t be very effective. Using RNN+LSTM good results can be achieved by pre-processing the data using the usual approaches such as stemming, lemmitization, stop word filtering, tokenization etc. But the same cannot be applied to the text data I have.
Would RNN and LSTM still work on my data? If not which networks do you guys suggest I explore for such a task?