情绪得分斯坦福核心 NLP

Question

我们如何使用斯坦福核心 NLP 获得完整句子的情感得分？

它将完整的句子分为正面和负面情绪，但是我们可以从斯坦福 NLP 工具中获得总情绪分数吗？

Answer 1

我所做的是根据句子长度平均每个句子的分数。其背后的逻辑是，较长的句子应该比较短的句子更有分量。

代码如下所示：

String line = "Great item! HDMI and decent wifi required as with all streaming devices.\n" +
            "The flow on the homepage is very good and responsive. Watching a series is a doddle, flow is great, no action required.\n" +
            "The remote and controller app both work a treat.\n" +
            "I really like this device.\n" +
            "I'd like to see an Amazon-written mirroring app available for non-Amazon products but no-one likes talking to each other in this field!";

    Long textLength = 0L;
    int sumOfValues = 0;

    Properties props = new Properties();
    props.setProperty("annotators", "tokenize, ssplit, parse, sentiment");
    StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
    int mainSentiment = 0;
    if (line != null && line.length() > 0) {
        int longest = 0;
        Annotation annotation = pipeline.process(line);
        for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
            Tree tree = sentence.get(SentimentCoreAnnotations.AnnotatedTree.class);
            int sentiment = RNNCoreAnnotations.getPredictedClass(tree);
            String partText = sentence.toString();
            if (partText.length() > longest) {
                textLength += partText.length();
                sumOfValues = sumOfValues + sentiment * partText.length();

                System.out.println(sentiment + " " + partText);
            }
        }
    }

    System.out.println("Overall: " + (double)sumOfValues/textLength);

下载整个项目here

Answer 2

给定句子，once 可以平均标注后的预测类别。 可以通过将带注释的树类传递给RNNCoreAnnotations.getPredictedClass 来访问预测的类：

public class SentimentAnalysis {
    private final String document;

    public SentimentAnalysis(String document) {
        this.document = document;
    }

    public SentimentAnalysis(Collection<String> sentences) {
        this.document = Joiner.on(". ").join(sentences);
    }

    public List<Integer> annotateAndScore(){
        List<Integer> scores = Lists.newArrayList();
        Properties props = new Properties();
        props.put("annotators", "tokenize, ssplit, pos, parse, sentiment");
        StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
        Annotation document = new Annotation(this.document);
        pipeline.annotate(document);
        for (CoreMap sentence : document.get(CoreAnnotations.SentencesAnnotation.class)) {
            Tree annotatedTree = sentence.get(SentimentCoreAnnotations.AnnotatedTree.class);
            int score = RNNCoreAnnotations.getPredictedClass(annotatedTree);
            scores.add(score);
        }
        return scores;
    }
}

Answer 3

我正在从我的blog 获取情绪分析代码： 代码很简单：

1. 使用 SentimentCoreAnnotations.SentimentClass.class 获取情绪 类型。
2. 使用 RNNCoreAnnotations.getPredictedClass 获取情绪 得分

情绪分析器

package com.interviewBubble.sentimentanalysis;

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.trees.Tree;

import edu.stanford.nlp.util.CoreMap;

public class SentimentAnalyzer {

 StanfordCoreNLP pipeline;

 public void initialize() {

  Properties properties = new Properties();

  properties.setProperty("annotators", "tokenize, ssplit, parse, sentiment");

  pipeline = new StanfordCoreNLP(properties);

 }

 public SentimentResult getSentimentResult(String text) {

  SentimentClassification classification = new SentimentClassification();

  SentimentResult sentimentResult = new SentimentResult();

  if (text != null && text.length() > 0) {

   Annotation annotation = pipeline.process(text);

   for(CoreMap sentence: annotation.get(CoreAnnotations.SentencesAnnotation.class)) {

       // System.out.println(sentence);

   Tree tree = sentence.get(SentimentCoreAnnotations.SentimentAnnotatedTree.class);

   //System.out.println(tree);

   SimpleMatrix simpleMatrix = RNNCoreAnnotations.getPredictions(tree);

   //System.out.println(simpleMatrix);

   classification.setVeryNegative((int)Math.round(simpleMatrix.get(0)*100d));

   classification.setNegative((int)Math.round(simpleMatrix.get(1)*100d));

   classification.setNeutral((int)Math.round(simpleMatrix.get(2)*100d));

   classification.setPositive((int)Math.round(simpleMatrix.get(3)*100d));

   classification.setVeryPositive((int)Math.round(simpleMatrix.get(4)*100d));

   String setimentType = sentence.get(SentimentCoreAnnotations.SentimentClass.class);

   sentimentResult.setSentimentType(setimentType);

   sentimentResult.setSentimentClass(classification);

   sentimentResult.setSentimentScore(RNNCoreAnnotations.getPredictedClass(tree));

   }

  }

  return sentimentResult;

 }

}

情绪分析：

package com.interviewBubble.sentimentanalysis;

public class SentimentAnalysis {

 public static void main(String[] args) {

     // I have taken iPhone X (Silver) User Review from Amazon

     String text = "Just love the X. Feel so Premium and a Head turner too. Face ID working fine but still miss "

       + "the fingerprint scanner very much. I jump from 5S to X so it’s a huge skip. I’m very very happy"

       + " with it. Specially battery backup is great after using with 4g cellular network and no heating "

       + "issue at all, though I’m not a mobile gamer, Oftentimes I play Boom Beach and I watch YouTube "

       + "videos and I surf a lot. It makes a deep hole in pocket at the Hefty price tag. So it’s all "

       + "upto your Consideration.\n";

     SentimentAnalyzer sentimentAnalyzer = new SentimentAnalyzer();

     sentimentAnalyzer.initialize();

     SentimentResult sentimentResult = sentimentAnalyzer.getSentimentResult(text);



     System.out.println("Sentiments Classification:");

  System.out.println("Very positive: " + sentimentResult.getSentimentClass().getVeryPositive()+"%");

  System.out.println("Positive: " + sentimentResult.getSentimentClass().getPositive()+"%");

  System.out.println("Neutral: " + sentimentResult.getSentimentClass().getNeutral()+"%");

  System.out.println("Negative: " + sentimentResult.getSentimentClass().getNegative()+"%");

  System.out.println("Very negative: " + sentimentResult.getSentimentClass().getVeryNegative()+"%");

     System.out.println("\nSentiments result:");

     System.out.println("Sentiment Score: " + sentimentResult.getSentimentScore());

  System.out.println("Sentiment Type: " + sentimentResult.getSentimentType());

 }

}

输出：

0    [main] INFO  edu.stanford.nlp.pipeline.StanfordCoreNLP  - Adding annotator tokenize

8    [main] INFO  edu.stanford.nlp.pipeline.TokenizerAnnotator  - No tokenizer type provided. Defaulting to PTBTokenizer.

12   [main] INFO  edu.stanford.nlp.pipeline.StanfordCoreNLP  - Adding annotator ssplit

17   [main] INFO  edu.stanford.nlp.pipeline.StanfordCoreNLP  - Adding annotator parse

512  [main] INFO  edu.stanford.nlp.parser.common.ParserGrammar  - Loading parser from serialized file edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz ... done [0.5 sec].

517  [main] INFO  edu.stanford.nlp.pipeline.StanfordCoreNLP  - Adding annotator sentiment

Sentiments Classification:

Very positive: 4%

Positive: 23%

Neutral: 24%

Negative: 39%

Very negative: 8%

Sentiments result:

Sentiment Score: 1

Sentiment Type: Negative

查找整个项目here