title: NLP - amazon reviews feature extraction
body: <p>I'm working on the amazon reviews dataset.
  the goal is to extract the positive and negative features of each product. </p>
  
  <p>for example: for the sentence "this product has great battery life" I would like to extract the word 'battery' as a positive feature.</p>
  
  <p>the dataset contains the fields:</p>
  
  <p><strong>reviewerID</strong> - ID of the reviewer, e.g. A2SUAM1J3GNN3B</p>
  
  <p><strong>asin</strong> - ID of the product, e.g. 0000013714</p>
  
  <p><strong>reviewerName</strong> - name of the reviewer</p>
  
  <p><strong>helpful</strong> - helpfulness rating of the review, e.g. 2/3</p>
  
  <p><strong>reviewText</strong> - text of the review</p>
  
  <p><strong>overall</strong> - rating of the product</p>
  
  <p><strong>summary</strong> - summary of the review</p>
  
  <p><strong>unixReviewTime</strong> - time of the review (unix time)</p>
  
  <p><strong>reviewTime</strong> - time of the review (raw)</p>
  
  <p>So far I separated the reviews for two lists: positive_reviews and negative_reviews.</p>
  
  <p>positive_reviews = all the reviews that have a rating > 3
  , negative_reviews = all the reviews that have a rating &lt; 3</p>
  
  <p>I cleaned the text and tokenized it. after that, I extracted the nouns that came to after adjectives hoping that those specific nouns will be the features that I'm looking for.
  after that, I tried to use clustering algorithms (k-means, DBSCAN) hoping that it will create a group that will represent the features I want to extract. </p>
  
  <p>The results aren't good at all and I'm hoping that someone here might have an idea for a way for this to work</p>