Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker.

In this article we will discuss Standford Sentiment analysis with example.

Standfornd Sentiment analysis build on new type of Recursive Neural Network that builds on top of grammatical structures.

Classes of sentiment classification

There are 5 classes of sentiment classification:

  1. very negative
  2. negative,
  3. neutral,
  4. positive,
  5.  very positive.


Need to add tokenize,ssplit , parse and sentiment annonators to get sentitment of a given text.

Properties props = new Properties();
     props.setProperty("annotators", "tokenize, ssplit, parse, sentiment");
     StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

     // negativeText
     // String text = "This movie doesn't care about cleverness, wit or any other kind of intelligent humor.";
     //Positive text
     //String text = "This movie is very good. I appriciate the way all the actors works.";
     //very positive text
     String text = "This movie is very good and one of my best movie. actors does best works.";
     int mainSentiment = 0;
     Annotation annotation = pipeline.process(text);
     int longest = 0;
     for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
         Tree tree = sentence.get(SentimentCoreAnnotations.SentimentAnnotatedTree.class);
         int sentiment = RNNCoreAnnotations.getPredictedClass(tree);
         String partText = sentence.toString();
         if (partText.length() > longest) {
             mainSentiment = sentiment;
             longest = partText.length();
     switch (mainSentiment){
         case 0:
             System.out.println("Very Negative");
         case 1:
         case 2:
         case 3:
         case 4:
             System.out.println("Very Positive");


This movie is very good and one of my best movie. actors does best works.
Very Positive

Refer Live Demo , Deep Learning for more details.

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