情感分析java库
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Sentiment Analysis java Library
提问by Jimmysnn
I have some unlabeled microblogging posts and I want to create a sentiment analysis module.
我有一些未标记的微博帖子，我想创建一个情绪分析模块。
To do this I have try Stanford libraryand Alchemy Apiweb service but the result it is not very good. For now I don't want training my classifier.
为此，我尝试了斯坦福图书馆和Alchemy Api网络服务，但结果不是很好。现在我不想训练我的分类器。
So I would like to suggest me some libraries or some web services about that. I would prefer a tested Library. The language of this posts is English. Also the preprocessing has been done.
所以我想向我推荐一些关于这方面的图书馆或一些网络服务。我更喜欢经过测试的库。这篇文章的语言是英语。也进行了预处理。
P.S.
聚苯乙烯
The programing language that I use is Java EE
我使用的编程语言是 Java EE
采纳答案by Marlon
If you want a good sentiment analysis service and you don't want to train your own classifier, you have to pay for it. However, it's worth mentioning that don't exist perfect tools in this field. There aren't tools that guarantee 100% of accuracy in their analysis.
如果你想要一个好的情感分析服务，而你又不想训练自己的分类器，你就必须为此付费。然而，值得一提的是，该领域并不存在完美的工具。没有工具可以保证其分析 100% 的准确性。
Having said that, a couple of months ago I played around with Semantria/Lexalytics. They have a straightforward Java SDK and a good accuracy on their sentiment analysis results.
话虽如此，几个月前我在玩Semantria/Lexalytics。他们有一个简单的 Java SDK，并且他们的情绪分析结果具有很好的准确性。
回答by Has QUIT--Anony-Mousse
Sentiment analysis doensn't keep up with the hyped promises.
情绪分析跟不上大肆宣传的承诺。
See e.g.
见例如
The Sad State of Sentiment Analysis
December 26, 2013 by Angela Hausman
http://www.hausmanmarketingletter.com/sad-state-sentiment-analysis/
情绪分析的悲伤状态
2013 年 12 月 26 日，Angela Hausman
http://www.hausmanmarketingletter.com/sad-state-sentiment-analysis/
Recent experiments suggest sentiment analysis data is LESS accurate than a coin toss (accuracy 50%). That's really scary if your brand makes strategic decisions based on sentiment analysis.
最近的实验表明，情绪分析数据不如掷硬币准确（准确率 50%）。如果您的品牌根据情绪分析做出战略决策，那真的很可怕。
...
...
While the tools accurately predicted between 60 and 80% of utterances, when neutral utterances were removed (80% of the utterances) the accuracy dropped alarmingly.
虽然这些工具准确地预测了 60% 到 80% 的话语，但当删除中性话语（80% 的话语）时，准确率急剧下降。
In other words, everybody is cheating on their benchmarks, and overfitting (e.g. tweets have tons of duplicates and near duplicates - retweets - if you include these, you are overestimating the real performance)
换句话说，每个人都在他们的基准上作弊，并且过度拟合（例如，推文有大量重复和接近重复的 - 转发 - 如果包括这些，你就高估了实际表现）
回答by Sam
LingPipe is a free(as well as paid) tool available for Sentiment Analysis. http://alias-i.com/lingpipe/index.html
LingPipe 是一种可用于情绪分析的免费（以及付费）工具。 http://alias-i.com/lingpipe/index.html
Main features include:
主要特点包括：
Sentiment Analysis
Named Entity Recognition
Clustering
Topic Classification
Language Identification
情绪分析
命名实体识别
聚类
话题分类
语言识别
etc
等等
回答by Hamdi
Here check SentiStrength: http://sentistrength.wlv.ac.uk/
在这里检查 SentiStrength：http://sentistrength.wlv.ac.uk/
They claim that it works with tweets.
他们声称它适用于推文。
