title: NLP analysis advice
body: <p>Working on an NLP project and would really benefit from any expert help.</p>
  <p>I'm looking to narrow down my options and select the most appropriate analysis methods and techniques for a project I'm working on. My question relates to what I should do in relation to the data I have. Any help (for a newbie) is very appreciated.</p>
  <p>My data: Open text, short string data responses to a survey question. I have multiple survey responses, each survey has a high number of respondents (3K+) although a relatively low number respond to the question (typically 50 per survey). The responses are short (typically one line/sentence response), but I have about 20 surveys, so a reasonable corpus to work with.</p>
  <p>Here's what I was planning (high level): Preprocess and clean the data, run some descriptives on the text data itself (BOWS, word frequency, maybe tf-idf, word clouds), then attempt some Topic Modelling and maybe Sentiment Analysis.</p>
  <p>My main questions as I work my way through this massive learning process:</p>
  <p>Would this type of data set warrant any particular Topic Modelling or Sentiment Analysis techniques?
  Are there any obvious or less obvious limitations or considerations I should keep in mind, as a result of the data I've got?
  Are there any clear step by step guides you can recommend? (I've been dipping in and out of a lot course and reading, but any similar experiences or examples would be invaluable).</p>
  <p>I appreciate this is a bit text heavy and asking a lot, but any help and support would be really fantastic.</p>