运行我的代码时收到以下错误：
The method get(Class) is undefined for the type String.
我理解为什么我会遇到这个问题，因为get（）方法不能在String上运行。但是，我希望有人可以告诉我如何解决这个问题？非常感谢所有建议。我遇到问题的代码部分是：
public static void averageSent(String review)
{
//populate sentence array with each sentence of the review
String [] sentences = review.split("[!?.]+");
//number of sentences in each review
int review_sentences = review.split("[!?.]+").length;
//array of sentiments for each review
String sentiments[] = new String[review_sentences];
//initialise total
int total = 0;
//populate sentiments array
for (int i=0; i< review_sentences; i++)
{
**sentiments[i]=sentences[i].get(SentimentCoreAnnotations.SentimentClass.class).toString();**
}
//output sentiments array
for (int i =0; i < sentiments.length; i++)
{
System.out.println("SENTIMENTS" +sentiments[i]);
}
//total up a score for the array
for (String s: sentiments)
{
...
}
}
整个代码是：
import java.io.*;
import java.util.*;
import edu.stanford.nlp.coref.CorefCoreAnnotations;
import edu.stanford.nlp.coref.data.CorefChain;
import edu.stanford.nlp.io.*;
import edu.stanford.nlp.io.EncodingPrintWriter.out;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.pipeline.CoreNLPProtos.Sentiment;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations;
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;
import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.util.*;
import edu.stanford.nlp.sentiment.Evaluate;
import org.apache.commons.io.FileUtils;
/** This class demonstrates building and using a Stanford CoreNLP pipeline. */
public class sentimentMain {
/** Usage: java -cp "*" StanfordCoreNlpDemo [inputFile [outputTextFile [outputXmlFile]]] */
public static void main(String[] args) throws IOException {
//ArrayList<String> Sentences = new ArrayList<String>();
//ArrayList<String> sentence_sentiment = new ArrayList<String>();
// set up optional output files
PrintWriter out;
if (args.length > 1) {
out = new PrintWriter(args[1]);
} else {
out = new PrintWriter(System.out);
}
PrintWriter xmlOut = null;
if (args.length > 2) {
xmlOut = new PrintWriter(args[2]);
}
// Add in sentiment
Properties props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref, sentiment");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
File[] files = new File("C:/stanford-corenlp-full-2016-10-31/dataset").listFiles();
String line = null;
try{
for (File file : files) {
if (file.exists()) {
BufferedReader in = new BufferedReader(new FileReader(file));
String str = FileUtils.readFileToString(file);
while((line = in.readLine()) != null)
{
Annotation document = new Annotation(line);
// run all the selected Annotators on this text
pipeline.annotate(document);
// this prints out the results of sentence analysis to file(s) in good formats
pipeline.prettyPrint(document, out);
if (xmlOut != null) {
pipeline.xmlPrint(document, xmlOut);
}
List<CoreMap> sentences = document.get(CoreAnnotations.SentencesAnnotation.class);
if (sentences != null && ! sentences.isEmpty()) {
CoreMap sentence = sentences.get(0);
for (CoreMap token : sentence.get(CoreAnnotations.TokensAnnotation.class)) {
}
Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
tree.pennPrint(out);
SemanticGraph graph = sentence.get(SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation.class);
Map<Integer, CorefChain> corefChains =
document.get(CorefCoreAnnotations.CorefChainAnnotation.class);
if (corefChains == null) { return; }
for (Map.Entry<Integer,CorefChain> entry: corefChains.entrySet()) {
for (CorefChain.CorefMention m : entry.getValue().getMentionsInTextualOrder()) {
// We need to subtract one since the indices count from 1 but the Lists start from 0
List<CoreLabel> tokens = sentences.get(m.sentNum - 1).get(CoreAnnotations.TokensAnnotation.class);
}
}
out.println("The first sentence overall sentiment rating is " + sentence.get(SentimentCoreAnnotations.SentimentClass.class));
}
}
//Sentences.forEach(s -> System.out.println(s));
//sentence_sentiment.forEach(s -> System.out.println(s));
averageSent(str);
in.close();
} else {
System.out.println("File: " + file.getName() + file.toString());
}
}
}catch(NullPointerException e){
e.printStackTrace();
}
IOUtils.closeIgnoringExceptions(out);
IOUtils.closeIgnoringExceptions(xmlOut);
}
public static void averageSent(String review)
{
//populate sentence array with each sentence of the review
String [] sentences = review.split("[!?.]+");
//number of sentences in each review
int review_sentences = review.split("[!?.]+").length;
//array of sentiments for each review
String sentiments[] = new String[review_sentences];
//initialise total
int total = 0;
//populate sentiments array
for (int i=0; i< review_sentences; i++)
{
sentiments[i]=sentences[i].get(SentimentCoreAnnotations.SentimentClass.class).toString();
}
//output sentiments array
for (int i =0; i < sentiments.length; i++)
{
System.out.println("SENTIMENTS" +sentiments[i]);
}
//total up a score for the array
for (String s: sentiments)
{
if (s.equals("Very positive"))
{
int veryPos = 4;
total += veryPos;
}
else if (s.equals("Positive"))
{
int pos = 3;
total += pos;
}
else if (s.equals("Negative"))
{
int neg = 2;
total += neg;
}
else if (s.equals("Very negative"))
{
int veryNeg = 1;
total += veryNeg;
}
else if (s.equals("Neutral"))
{
int neu = 0;
total += neu;
}
//System.out.println("Total " +total);
}
}
}
使用的主要代码是Stanford CoreNLP情绪分析模型。然而，由于这个模型只能给我电影评论中个别句子的情绪，我设计了这种方法，这样我就可以得到整个评论的平均情绪。 get（）方法返回句子的情绪。如果对象来自CoreMap，它可以工作，但我需要它来获取字符串的情绪，这样它就可以为每个电影评论填充多个情绪的字符串数组。
答案 0 :(得分：0)
以下是一些示例代码，用于了解每种情绪的情绪并计算“正面”，“否定”和“中立”句子。
package edu.stanford.nlp.examples;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.sentiment.*;
import edu.stanford.nlp.util.*;
import java.util.Properties;
public class AverageDocumentSentiment {
public static void main(String[] args) {
Annotation document =
new Annotation("...movie review text...");
Properties props = new Properties();
props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse,sentiment");
props.setProperty("parse.binaryTrees","true");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
pipeline.annotate(document);
int totalNegative = 0;
int totalPositive = 0;
int totalNeutral = 0;
int total = 0;
for (CoreMap sentence : document.get(CoreAnnotations.SentencesAnnotation.class)) {
String sentenceSentiment = sentence.get(SentimentCoreAnnotations.SentimentClass.class);
System.out.println(sentenceSentiment);
if (sentenceSentiment.equals("Negative")) {
totalNegative++;
} else if (sentenceSentiment.equals("Positive")) {
totalPositive++;
} else {
totalNeutral++;
}
total++ ;
}
}
}