<p>You can start by reading some papers on sarcasm detection in twitter, e.g. <a href="http://www.aclweb.org/anthology/W10-2914" rel="nofollow">Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon</a>, which uses patterns of content words and high frequency words, or closer to your question <a href="http://www.aclweb.org/anthology/D15-1116" rel="nofollow">Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic
Meaning of Words</a> which uses <code>word2vec</code>. The latter views the sarcasm detection problem as disambiguation problem of literal and sarcastic meanings of the same word. Perhaps you can employ this approach using the recently published <a href="http://arxiv.org/pdf/1511.06388v1.pdf" rel="nofollow">sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings</a>.</p>

<p>Try to use the techniques used in the papers, and when you encounter a <em>specific</em> problem ask a question with a minimal working example. </p>
Unsure about word embeddings, POS re: using a Neural Net for NLP classification.
<p>Im planning on using an NN for sarcasm detection on a number of tweets. Im unsure of how to prepare the word embeddings I will train the NN on. If I tokenize the strings and tag emoticons, capitalisation, user tags, hashtags etc, how do i then combine the resulting strings with word embeddings? do i train the word embeddings on the resulting corpus of tweets?</p>