Classifying Texts and Documents - Natural Language Processing with Java - page 228

+ "an odd fellow.";
String mary = "Mary thought that custard pie was the "
+ "best pie in the world. However, she loathed "
+ "chocolate pie.";
To perform this analysis, we need to use a sentiment annotator as shown here. This
also requires the use of the tokenize , ssplit and parse annotators. The parse an-
notator provides more structural information about the text, which will be discussed in
more detail in Chapter 7 , Using a Parser to Extract Relationships :
Properties props = new Properties();
props.put("annotators", "tokenize, ssplit, parse,
sentiment");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
The text is used to create an Annotation instance, which is then used as the argument
to the annotate method that performs the actual work, as shown here:
Annotation annotation = new Annotation(review);
pipeline.annotate(annotation);
The following array holds the strings for the different sentiments possible:
String[] sentimentText = {"Very Negative", "Negative",
"Neutral", "Positive", "Very Positive"};
The Annotation class' get method returns an object that implements the CoreMap
interface. In this case, these objects represent the results of splitting the input text into sen-
tences, as shown in the following code. For each sentence, an instance of a Tree object is
obtained that represents a tree structure containing a parse of the text for the sentiment.
The getPredictedClass method returns an index into the sentimentText array
reflecting the sentiment of the test:
for (CoreMap sentence : annotation.get(
CoreAnnotations.SentencesAnnotation.class)) {
Tree tree = sentence.get(
SentimentCoreAnnotations.AnnotatedTree.class);
int score = RNNCoreAnnotations.getPredictedClass(tree);