我使用斯坦福NLPv3.6（Java）来计算英语句子的情绪。
斯坦福NLP计算从0到4的句子的极性。
0非常消极
1否定
2中性
3正
4非常积极
我运行了一些非常简单的测试用例，但是得到了非常奇怪的结果。
例子：
文=Jhon是好人，情=3（即积极的）
文本=大卫是好人，情感=2（即中立）
在上面的例子中，句子是相同的，除了名字David，Jhon，但是情感值是不同的这不是模棱两可吗？
我用这个Java代码计算情绪：
public static float calSentiment(String text) {
// pipeline must get initialized before proceeding further
Properties props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, parse, sentiment");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
int mainSentiment = 0;
if (text != null && text.length() > 0) {
int longest = 0;
Annotation annotation = pipeline.process(text);
for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
Tree tree = sentence.get(SentimentCoreAnnotations.SentimentAnnotatedTree.class);
int sentiment = RNNCoreAnnotations.getPredictedClass(tree);
String partText = sentence.toString();
if (partText.length() > longest) {
mainSentiment = sentiment;
longest = partText.length();
}
}
}
if (mainSentiment > 4 || mainSentiment < 0) {
return -9999;
}
return mainSentiment;
}
我在Java代码中遗漏了什么吗？
当句子是正面的时候，我也会有负面情绪（即少于2），反之亦然。
谢谢。
下面是我用简单英语句子得出的结果：
Sentence: Tendulkar is a great batsman
Sentiment: 3
Sentence: David is a great batsman
Sentiment: 3
Sentence: Tendulkar is not a great batsman
Sentiment: 1
Sentence: David is not a great batsman
Sentiment: 2
Sentence: Shyam is not a great batsman
Sentiment: 1
Sentence: Dhoni loves playing football
Sentiment: 3
Sentence: John, Julia loves playing football
Sentiment: 3
Sentence: Drake loves playing football
Sentiment: 3
Sentence: David loves playing football
Sentiment: 2
Sentence: Virat is a good boy
Sentiment: 2
Sentence: David is a good boy
Sentiment: 2
Sentence: Virat is not a good boy
Sentiment: 1
Sentence: David is not a good boy
Sentiment: 2
Sentence: I love every moment of life
Sentiment: 3
Sentence: I hate every moment of life
Sentiment: 2
Sentence: I like dancing and listening to music
Sentiment: 3
Sentence: Messi does not like to play cricket
Sentiment: 1
Sentence: This was the worst movie I have ever seen
Sentiment: 0
Sentence: I really appreciated the movie
Sentiment: 1
Sentence: I really appreciate the movie
Sentiment: 3
Sentence: Varun talks in a condescending way
Sentiment: 2
Sentence: Ram is angry he did not win the tournament
Sentiment: 1
Sentence: Today's dinner was awful
Sentiment: 1
Sentence: Johny is always complaining
Sentiment: 3
Sentence: Modi's demonetisation has been very controversial and confusing
Sentiment: 1
Sentence: People are left devastated by floods and droughts
Sentiment: 2
Sentence: Chahal did a fantastic job by getting the 6 wickets
Sentiment: 3
Sentence: England played terribly bad
Sentiment: 1
Sentence: Rahul Gandhi is a funny man
Sentiment: 3
Sentence: Always be grateful to those who are generous towards you
Sentiment: 3
Sentence: A friend in need is a friend indeed
Sentiment: 3
Sentence: Mary is a jubilant girl
Sentiment: 2
Sentence: There is so much of love and hatred in this world
Sentiment: 3
Sentence: Always be positive
Sentiment: 3
Sentence: Always be negative
Sentiment: 1
Sentence: Never be negative
Sentiment: 1
Sentence: Stop complaining and start doing something
Sentiment: 2
Sentence: He is a awesome thief
Sentiment: 3
Sentence: Ram did unbelievably well in this year's exams
Sentiment: 2
Sentence: This product is well designed and easy to use
Sentiment: 3
最佳答案
情绪决策是由经过训练的神经网络来完成的。不幸的是，你在同一句话中提供的名字不同，它的表现也很奇怪，但这是意料之中的正如github上所讨论的，一个因素可能是各种名称在培训数据中并不经常出现。