好吧..所以我做了一些挖掘，并已开始最终了解（一些什么）作为如何创建一个数据集树，并将尝试打破它的任何人谁偶然发现这个职位与同样的麻烦，因为我一直在.
第1步。

• 找到你的数据（我的例子是关于英国房地产市场的新闻文章）

UK renters: are you living with someone you’ve fallen out with?
UK property asking prices stagnating, lifting hopes of softer landing for housing market

第二步。

• 注解数据

2 UK renters: are you living with someone you’ve fallen out with?
1 fallen out with
1 fallen out
2 UK renters
2 living with someone
3 fallen
2 :
2 ?
2 living with
2 someone

3 UK property asking prices stagnating, lifting hopes of softer landing for housing market
2 UK property
3 asking prices stagnating
2 asking prices
4 lifting hopes
2 hopes
4 lifting hopes of softer landing
3 softer landing for housing market
2 housing market
2 lifting
2 landing
2 ,

• 注解含义 *

Very Positive= 4
Positive = 3
Neutral = 2
Negative = 1
Very Negative = 0

• 结构 *

2 UK renters: are you living with someone you’ve fallen out with?
   //Overall sentiment

1 fallen out with
   // Negative

1 fallen out
   // Negative

2 UK renters
   // Neutral

...etc..

• 将注解数据保存为.txt（sample.txt）

第三步：

• 找到您的stanford-corenlp-4.5.2.jar
• • 示例 * ~/.m2/repository/edu/stanford/nlp/stanford-corenlp/4.5.2

第四步：

• 打开Bash并运行
• java -cp "*" -mx5g edu.stanford.nlp.sentiment.BuildBinarizedDataset -input /c/Users/rusku/Desktop/StanfordNPL/rusSample/sample.txt
• • 替换上述数据位置 *

第五步：

• 结果

(2 (2 (2 (2 UK) (2 renters)) (2 :)) (2 (2 (2 (2 are) (2 you)) (2 (2 living) (2 (2 with) (2 (2 someone) (2 (2 you) (2 (2 ▒ve) (1 (1 (3 fallen) (2 out)) (2 with)))))))) (2 ?)))
(3 (3 (2 (3 UK) (3 property)) (2 (3 asking) (3 prices))) (3 (3 (3 stagnating) (3 (2 ,) (4 (2 lifting) (2 hopes)))) (3 (3 of) (3 (3 (3 softer) (2 landing)) (3 (3 for) (2 (3 housing) (3 market)))))))

资源：Train Stanford CoreNLP about the sentiment of domain-specific phrases
这是我目前所得到的。
希望这个有用。