我是新斯坦福NLP。我找不到任何好的和完整的文档或教程。我的工作是做情绪分析。我有一个非常大的产品评论数据集。根据用户给出的“开始”，我已经通过正面和负面来区分它们。现在我需要找到最常出现的正面和负面形容词作为我算法的特征。我了解如何从here进行标记化，词形还原和POS分类。我有这样的文件。
评论是
Don't waste your money. This is a short DVD and the host is boring and offers information that is common sense to any idiot. Pass on this and buy something else. Very generic
，输出为。
Sentence #1 (6 tokens):
Don't waste your money.
[Text=Do CharacterOffsetBegin=0 CharacterOffsetEnd=2 PartOfSpeech=VBP Lemma=do]
[Text=n't CharacterOffsetBegin=2 CharacterOffsetEnd=5 PartOfSpeech=RB Lemma=not]
[Text=waste CharacterOffsetBegin=6 CharacterOffsetEnd=11 PartOfSpeech=VB Lemma=waste]
[Text=your CharacterOffsetBegin=12 CharacterOffsetEnd=16 PartOfSpeech=PRP$ Lemma=you]
[Text=money CharacterOffsetBegin=17 CharacterOffsetEnd=22 PartOfSpeech=NN Lemma=money]
[Text=. CharacterOffsetBegin=22 CharacterOffsetEnd=23 PartOfSpeech=. Lemma=.]
Sentence #2 (21 tokens):
This is a short DVD and the host is boring and offers information that is common sense to any idiot.
[Text=This CharacterOffsetBegin=24 CharacterOffsetEnd=28 PartOfSpeech=DT Lemma=this]
[Text=is CharacterOffsetBegin=29 CharacterOffsetEnd=31 PartOfSpeech=VBZ Lemma=be]
[Text=a CharacterOffsetBegin=32 CharacterOffsetEnd=33 PartOfSpeech=DT Lemma=a]
[Text=short CharacterOffsetBegin=34 CharacterOffsetEnd=39 PartOfSpeech=JJ Lemma=short]
[Text=DVD CharacterOffsetBegin=40 CharacterOffsetEnd=43 PartOfSpeech=NN Lemma=dvd]
[Text=and CharacterOffsetBegin=44 CharacterOffsetEnd=47 PartOfSpeech=CC Lemma=and]
[Text=the CharacterOffsetBegin=48 CharacterOffsetEnd=51 PartOfSpeech=DT Lemma=the]
[Text=host CharacterOffsetBegin=52 CharacterOffsetEnd=56 PartOfSpeech=NN Lemma=host]
[Text=is CharacterOffsetBegin=57 CharacterOffsetEnd=59 PartOfSpeech=VBZ Lemma=be]
[Text=boring CharacterOffsetBegin=60 CharacterOffsetEnd=66 PartOfSpeech=JJ Lemma=boring]
[Text=and CharacterOffsetBegin=67 CharacterOffsetEnd=70 PartOfSpeech=CC Lemma=and]
[Text=offers CharacterOffsetBegin=71 CharacterOffsetEnd=77 PartOfSpeech=VBZ Lemma=offer]
[Text=information CharacterOffsetBegin=78 CharacterOffsetEnd=89 PartOfSpeech=NN Lemma=information]
[Text=that CharacterOffsetBegin=90 CharacterOffsetEnd=94 PartOfSpeech=WDT Lemma=that]
[Text=is CharacterOffsetBegin=95 CharacterOffsetEnd=97 PartOfSpeech=VBZ Lemma=be]
[Text=common CharacterOffsetBegin=98 CharacterOffsetEnd=104 PartOfSpeech=JJ Lemma=common]
[Text=sense CharacterOffsetBegin=105 CharacterOffsetEnd=110 PartOfSpeech=NN Lemma=sense]
[Text=to CharacterOffsetBegin=111 CharacterOffsetEnd=113 PartOfSpeech=TO Lemma=to]
[Text=any CharacterOffsetBegin=114 CharacterOffsetEnd=117 PartOfSpeech=DT Lemma=any]
[Text=idiot CharacterOffsetBegin=118 CharacterOffsetEnd=123 PartOfSpeech=NN Lemma=idiot]
[Text=. CharacterOffsetBegin=123 CharacterOffsetEnd=124 PartOfSpeech=. Lemma=.]
Sentence #3 (8 tokens):
Pass on this and buy something else.
[Text=Pass CharacterOffsetBegin=125 CharacterOffsetEnd=129 PartOfSpeech=VB Lemma=pass]
[Text=on CharacterOffsetBegin=130 CharacterOffsetEnd=132 PartOfSpeech=IN Lemma=on]
[Text=this CharacterOffsetBegin=133 CharacterOffsetEnd=137 PartOfSpeech=DT Lemma=this]
[Text=and CharacterOffsetBegin=138 CharacterOffsetEnd=141 PartOfSpeech=CC Lemma=and]
[Text=buy CharacterOffsetBegin=142 CharacterOffsetEnd=145 PartOfSpeech=VB Lemma=buy]
[Text=something CharacterOffsetBegin=146 CharacterOffsetEnd=155 PartOfSpeech=NN Lemma=something]
[Text=else CharacterOffsetBegin=156 CharacterOffsetEnd=160 PartOfSpeech=RB Lemma=else]
[Text=. CharacterOffsetBegin=160 CharacterOffsetEnd=161 PartOfSpeech=. Lemma=.]
Sentence #4 (2 tokens):
Very generic
[Text=Very CharacterOffsetBegin=162 CharacterOffsetEnd=166 PartOfSpeech=RB Lemma=very]
[Text=generic CharacterOffsetBegin=167 CharacterOffsetEnd=174 PartOfSpeech=JJ Lemma=generic]
我已经处理了10000正面和10000负面文件。现在，我如何才能轻松找到最常见的正面和负面特征（形容词）？我是否需要阅读所有输出（处理过的）文件并制作这样的形容词的列表频率计数，或者stanford corenlp有什么简单的方法吗？
答案 0 :(得分：1)
以下是处理带注释的评论并将形容词存储在计数器中的示例。
在电影评论的例子中，电影很棒！这是一部很棒的电影。＆＃34;有一种积极的情绪＆＃34;。
我建议更改我的代码以加载每个文件，并使用文件的文本构建一个Annotation并记录该文件的情绪。
然后你可以浏览每个文件并建立一个计数器，每个形容词都有正数和负数。
最终的计数器有形容词＆＃34; great＆＃34;数为2。
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