Classifying whether a comment or review is a complaint or appreciation of product and extracting the Topic?

Your (basic) task is sentiment analysis, covered in many places.

There is a number of algorithms proven good for that, including LSTM but you need a good deal of data to train that (and compute power). Others is fastText - tool by Facebook - where you have already embeddings for a number of languages including EN.

But you need anyway 'ground truth' - samples of positive and negative posts / reviews - to train final classification model. Assuming you have standard EN reviews - meaning a lot of language typical to online reviews... - you can use some of existing datasets to train your model and boost results. Otherwise you would have to manually select representative (meaning the more the better...) samples and label them (pos/neg). E.g. there is yelp review dataset, actually a number of versions, one of them.

The other tasks - extracting product and complaint/appreciation part - are entity extraction and topic modelling respectively, each a seprate story.

For entity (product) extraction, possibly you can just search the post for the names from your inventory. Assuming again EN language, beware only of plural versions. And you will need a good data structure / indexes for it to get results fast.

For topic modelling, there is LDA implementation in gensim. You will have to manually go through the topics and process them, depending on the exact scenario you want to use them, e.g. assign each a meaningful name, selecting meanwhile those that make sense for you, merging some.