2
私は英語文章の感情を計算するスタンフォードNLPのV3.6（JAVA）を使用しています。スタンフォードNLP心理あいまいな結果
スタンフォードNLPは4
- 0非常に負
- 1負
- 2中性
- 3正
- 4非常にポジティブ
私はいくつかの非常に単純なテストケースを実行しましたが、その結果。
例：=ダビデは良い人で、感情= 2（すなわちニュートラル）
- テキスト= JHONが良い人で、感情= 3（すなわち正）
- テキスト
上記の例では、文章は同じで、名前はDavid,Jhonですが、感想の値が異なります。 このあいまいさはありません？
私は感情を計算するため、このJavaコードを使用：
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;
}
は、私は、Javaコードで何かをしないのですか？
私はまた、文が肯定的である場合には否定的な感情（すなわち、2未満）を、逆の場合は否定的な感情を得た。
ありがとうございました。感情の決定は訓練されたニューラルネットワークによって作られています
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
バージョン3.7.0とPythonで同様の結果が得られません。私はこれがバグだと思う。 – sds
https://github.com/stanfordnlp/CoreNLP/issues/351を参照してください。 – sds