Text analysis: structure and sentiment
So, I understand from this question that you want to understand the grammatical structure of these messages, as well as the sentiment. 

In terms of grammatical structure, this depends on how far you want to parse the grammatical structure, whether that is as simple as Part of Speech (POS) tagging (Noun, Verb, etc.) or if you need to parse the sentences as effectively parse trees, which show how each word depends on others.

For POS tagging, you can use nltk's POS tagging methods (link: https://www.nltk.org/book/ch05.html)

To get an idea of how each words relates to each other, one way to do it would be dependency parsing (principles are shown here: https://medium.com/@5hirish/dependency-parsing-in-nlp-d7ade014186)

In terms of sentiment analysis, there are many ways you can approach this. One approach I can think of from the top of my head would be using a encoder-decoder model architecture. 
In this type of architecture, we firstly encode the input text as a sequence of tokens (using something like an RNN / LSTM) into a 'hidden representation'. Then we decode this 'hidden representation' using for example a standard neural network where we have a final softmax output layer, which gives a probability distribution over sentiment classes. The sentiment is then chosen as the index with the highest probability. 
Here is a really good paper, which talks about sentiment analysis using an encoder-decoder architecture, which could be of some use: https://www.aclweb.org/anthology/Q18-1002/