title: Can LDA model be useful for sentences (not documents) clustering / classification?
body: <p>Recently, I’m working on sentence classification problem, these sentences are nothing but one or two line of reviews about product and customers post there feedback on various features that product has to offer. After pre-processing (removal of stop words and stemming)  I’m using feature extraction libraries  (like word2vec, tf-idf) and clustering algorithms (k-mean) to run over my sentences to have unsupervised sentence classification - output is fairly acceptable. However I’m looking for more options on clustering algorithm, specifically wanted to try out LDA to  further improve quality of output however I have come across <a href="https://www-ljk.imag.fr/membres/Marianne.Clausel/Fichiers/SentenceLDA.pdf" rel="nofollow noreferrer">this</a> paper listing few facts on LDA for using on sentence classification.</p>
  <p>My question is – Would be helpful to use LDA on sentence (not documents) classification? Also apart from K-mean what are other alternative with unsupervised learning that that can work well with sentence classification. Thank you in advance for all your suggestion.</p>
  <p>Note: I’m practicing my exercise in Spark 1.6.1 environment with pyspark API.</p>
  <h1>After Trying out LDA by myself, below is output:</h1>
  <p>1 Topics came out similar: frequent words for each of the topics overlap a lot and topics share almost the same set of words.</p>
  <p><a href="https://i.stack.imgur.com/BmR6r.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/BmR6r.png" alt="enter image description here" /></a></p>
  <p>One of my understanding was, my reviews belongs to specific domain. For example my product belong to credit card domain &amp; all reviews revolving around this singl domain. Further, I tried to plot word distribution and found that most frequently use word is just around 2% of total population.</p>