Question

我使用 Stanford NLP v3.6 （ JAVA ）来计算英语句子的情绪。< / p>

斯坦福NLP计算从0到4的句子极性。

0非常消极
1个负面
2中立
3 positive
4非常积极

我运行了一些非常简单的测试用例但得到了非常奇怪的结果。

示例：

Text = Jhon是好人，情感= 3（即正面）
文字=大卫是好人，情感= 2（即中立）

在上面的示例中，句子相同，而名称David，Jhon除外，但情感值不同。 不是这种含糊不清？

我使用这个Java代码来计算情绪：

 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;

        }

我是否遗漏了java代码中的内容？

当句子为正时，我也有负面情绪（即小于2），反之亦然。

感谢。

以下是我用简单的英语句子得到的结果：

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

Answer 1

情绪决策由训练有素的神经网络决定。不幸的是，它基于你在同一个句子中提供的不同名称而表现得很奇怪，但这是可以预期的。正如在GitHub上讨论的那样，一个因素可能是各种名称在训练数据中不常出现。

斯坦福NLP情绪模糊不清

1 个答案: