Question

我使用Stanford core NLP库进行情绪分析。下面的代码返回一个示例的类，但我怎样才能获得分数？例如-0.3表示否定等

private int getScore(String line) {
    boolean isrun = false;
    StanfordCoreNLP pipeline = null;
    if(!isrun){
        Properties props = getProperties();
        pipeline = new StanfordCoreNLP(props);
        isrun = true;
    }
    Annotation annotation;

    int sentiment = -1;
    if (line != null && line.length() > 0) {
        annotation = pipeline.process(line);
        for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
            Tree tree = sentence.get(SentimentCoreAnnotations.AnnotatedTree.class);
            sentiment = RNNCoreAnnotations.getPredictedClass(tree);
        }
    }
    return sentiment;
}

修改

在线上demo当鼠标位于图表的根目录时，我们可以看到该示例为负72％。怎么能得到这个号码？

Answer 1

0.下载Stanford NLP Core Lib并导入外部lib stanford-corenlp-3.5.2-models.jar，stanford-corenlp-3.5.2.jar，stanford-corenlp-3.5.2-sources.jar和ejml- 0.23.jar进入这个包。

1.在Eclipse中构建此类NLP

import java.util.Properties;
import org.ejml.simple.SimpleMatrix;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations.SentimentAnnotatedTree;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.util.CoreMap;

public class NLP {
static StanfordCoreNLP pipeline;

public static void init() {
    Properties props = new Properties();
    props.setProperty("annotators", "tokenize, ssplit, parse, sentiment");
    pipeline = new StanfordCoreNLP(props);
}

public static int findSentiment(String tweet) {

    int mainSentiment = 0;
    if (tweet != null && tweet.length() > 0) {
        int longest = 0;
        Annotation annotation = pipeline.process(tweet);
        for (CoreMap sentence : annotation
                .get(CoreAnnotations.SentencesAnnotation.class)) {
            Tree tree = sentence
                    .get(SentimentAnnotatedTree.class);
            int sentiment = RNNCoreAnnotations.getPredictedClass(tree);
            SimpleMatrix sentiment_new = RNNCoreAnnotations.getPredictions(tree);             
            String partText = sentence.toString();
            if (partText.length() > longest) {
                mainSentiment = sentiment;
                longest = partText.length();
            }
        }
    }
    return mainSentiment;
    }
}

2.使用NLP

构建一个新类来解析你的句子

import java.util.ArrayList;

public class What2Think {

    public static void main(String[] args) {
        ArrayList<String> tweets = new ArrayList<String>();
        tweets.add("In this country, \"democracy\" means pro-government. #irony");
        NLP.init();
        for(String tweet : tweets) {
            System.out.println(tweet + " : " + NLP.findSentiment(tweet));
        }
    }
}

运行它

Answer 2

我有类似的要求。您可以从SimpleMatrix

获取此信息

 SimpleMatrix sm = RNNCoreAnnotations.getPredictions(tree);

如果您打印变量 sm ，则输出有类似的内容

Type = dense , numRows = 5 , numCols = 1
 0.111  
 0.758  
 0.087  
 0.035  
 0.009

这给出了估计的概率。在在线演示中，您可以在％。

中看到这些值

您可以找到我的实施here。

希望它有所帮助!!!

斯坦福情绪分析得分java

2 个答案: