ich Stanford NLP v3.6 bin mit (JAVA) Gefühl von Englisch Sätze zu berechnen.Stanford NLP Gefühl zwiespältiges Ergebnis
Stanford NLP berechnet die Polarität des Satzes von 0 bis 4.
- 0 sehr negative
- 1 negativ
- 2 neutral
- 3 positive
- 4 sehr positive
Ich habe ein paar sehr einfache Testfälle, aber sehr strat nge Ergebnis.
Beispiel:
- Text = Jhon ist guter Mensch, Sentiment = 3 (dh positiver)
- Text = David ist guter Mensch, Sentiment = 2 (dh neutral)
Im obigen Beispiel sind die Sätze gleich, andere, dass der Name David
, Jhon
, aber Sentiment Werte sind unterschiedlich. Ist diese Mehrdeutigkeit nicht?
ich verwendet, um dieses Java-Code für die Berechnung der Stimmung:
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;
}
Ist I etwas in Java-Code bin fehlt?
Ich bekam auch negative Stimmung (d. H. Weniger als 2), wenn der Satz positiv war und umgekehrt.
Danke.
Dies sind Ergebnisse, die ich mit einfachen englischen Sätzen bekam:
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
Ich bekomme ähnliche absurde Ergebnisse mit Version 3.7.0 und Python. Ich denke, das ist ein Fehler. – sds
Siehe https://github.com/stanfordnlp/CoreNLP/issues/351 – sds