我正在使用Stanford CoreNLP对一个目录中包含的25,000个单独的文本电影评论进行情绪分析。为了做到这一点，我需要略微改变斯坦福代码，因为它只分析单个文本文件中的每个单独的句子。
我尝试执行此操作如下：
import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import org.apache.commons.io.FileUtils;
import com.google.common.io.Files;
import edu.stanford.nlp.dcoref.CorefChain;
import edu.stanford.nlp.dcoref.CorefCoreAnnotations.CorefChainAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.NamedEntityTagAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.PartOfSpeechAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TextAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TokensAnnotation;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.TreeCoreAnnotations.TreeAnnotation;
import edu.stanford.nlp.util.CoreMap;
import java.io.File;
import java.util.Iterator;
import org.apache.commons.io.*;
/** A simple corenlp example ripped directly from the Stanford CoreNLP website using text from wikinews. */
public class sentimentMain {
public static void main(String[] args) throws IOException {
// creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution
Properties props = new Properties();
props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
// read some text from the file..
Iterator it = FileUtils.iterateFiles(new File("C:\\stanford-corenlp-full-2016-10-31\\train\\neg"), null, false);
Iterator it1 = FileUtils.iterateFiles(new File("C:\\stanford-corenlp-full-2016-10-31\\train\\pos"), null, false);
Iterator it2 = FileUtils.iterateFiles(new File("C:\\stanford-corenlp-full-2016-10-31\\train\\unsup"), null, false);
File inputFile = new File ((String) (it.next()));
String text = Files.toString(inputFile, Charset.forName("UTF-8"));
System.out.println(text);
//File inputFile = new File("C:/stanford-corenlp-full-2016-10-31/input.txt");
//String text = Files.toString(inputFile, Charset.forName("UTF-8"));
// create an empty Annotation just with the given text
Annotation document = new Annotation(text);
// run all Annotators on this text
pipeline.annotate(document);
// these are all the sentences in this document
// a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
List<CoreMap> sentences = document.get(SentencesAnnotation.class);
for(CoreMap sentence: sentences) {
// traversing the words in the current sentence
// a CoreLabel is a CoreMap with additional token-specific methods
for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
// this is the text of the token
String word = token.get(TextAnnotation.class);
// this is the POS tag of the token
String pos = token.get(PartOfSpeechAnnotation.class);
// this is the NER label of the token
String ne = token.get(NamedEntityTagAnnotation.class);
System.out.println("word: " + word + " pos: " + pos + " ne:" + ne);
}
// this is the parse tree of the current sentence
Tree tree = sentence.get(TreeAnnotation.class);
System.out.println("parse tree:\n" + tree);
// this is the Stanford dependency graph of the current sentence
SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);
System.out.println("dependency graph:\n" + dependencies);
}
// This is the coreference link graph
// Each chain stores a set of mentions that link to each other,
// along with a method for getting the most representative mention
// Both sentence and token offsets start at 1!
Map<Integer, CorefChain> graph =
document.get(CorefChainAnnotation.class);
}
}
我收到以下错误：
Exception in thread "main" java.lang.ClassCastException: java.io.File cannot be cast to java.lang.String
at sentimentMain.main(sentimentMain.java:46)
我理解“it.next（）”无法转换为字符串，但有没有人知道另一种方法我可以确保将文件内容作为字符串输入进行处理？
提前致谢：）
答案 0 :(得分：0)
它是一个直接的编译错误，一个体面的IDE会告诉你。 变量 - ＆＃34;文字＆＃34;在while循环之外不可用，它应该在while循环开始之前声明，或者文档声明应该在while循环中声明。
请在下面找到已编辑的代码。
import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import org.apache.commons.io.FileUtils;
import com.google.common.io.Files;
import edu.stanford.nlp.dcoref.CorefChain;
import edu.stanford.nlp.dcoref.CorefCoreAnnotations.CorefChainAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.NamedEntityTagAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.PartOfSpeechAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TextAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TokensAnnotation;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.TreeCoreAnnotations.TreeAnnotation;
import edu.stanford.nlp.util.CoreMap;
import java.io.File;
import java.util.Iterator;
import org.apache.commons.io.*;
/** A simple corenlp example ripped directly from the Stanford CoreNLP website using text from wikinews. */
public class sentimentMain {
public static void main(String[] args) throws IOException {
// creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution
Properties props = new Properties();
props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
// read some text from the file..
Iterator it = FileUtils.iterateFiles(new File("C:\\stanford-corenlp-full-2016-10-31\\train\\neg"), null, false);
Iterator it1 = FileUtils.iterateFiles(new File("C:\\stanford-corenlp-full-2016-10-31\\train\\pos"), null, false);
Iterator it2 = FileUtils.iterateFiles(new File("C:\\stanford-corenlp-full-2016-10-31\\train\\unsup"), null, false);
while(it.hasNext()){
File inputFile = new File ((String) (it.next()));
String text = Files.toString(inputFile, Charset.forName("UTF-8"));
System.out.println(text);
//File inputFile = new File("C:/stanford-corenlp-full-2016-10-31/input.txt");
//String text = Files.toString(inputFile, Charset.forName("UTF-8"));
// create an empty Annotation just with the given text
Annotation document = new Annotation(text);
// run all Annotators on this text
pipeline.annotate(document);
// these are all the sentences in this document
// a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
List<CoreMap> sentences = document.get(SentencesAnnotation.class);
for(CoreMap sentence: sentences) {
// traversing the words in the current sentence
// a CoreLabel is a CoreMap with additional token-specific methods
for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
// this is the text of the token
String word = token.get(TextAnnotation.class);
// this is the POS tag of the token
String pos = token.get(PartOfSpeechAnnotation.class);
// this is the NER label of the token
String ne = token.get(NamedEntityTagAnnotation.class);
System.out.println("word: " + word + " pos: " + pos + " ne:" + ne);
}
// this is the parse tree of the current sentence
Tree tree = sentence.get(TreeAnnotation.class);
System.out.println("parse tree:\n" + tree);
// this is the Stanford dependency graph of the current sentence
SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);
System.out.println("dependency graph:\n" + dependencies);
}
// This is the coreference link graph
// Each chain stores a set of mentions that link to each other,
// along with a method for getting the most representative mention
// Both sentence and token offsets start at 1!
Map<Integer, CorefChain> graph =
document.get(CorefChainAnnotation.class);
}
}
}