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I have some unlabeled microblogging posts and I want to create a sentiment analysis module.
To do this I have try Stanford library and Alchemy Api web service but the result it is not very good. For now I don't want training my classifier.
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.
P.S.
The programing language that I use is Java EE
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In Java code, the Stanford CoreNLP sentiment classifier is used as follows. To start, you build up a text processing pipeline by adding the annotators required to perform sentiment analysis, such as tokenize , ssplit , parse , and sentiment .
Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
Sentiment Analysis is a process of extracting opinions that have different scores like positive, negative or neutral. Based on sentiment analysis, you can find out the nature of opinion or sentences in text.
Sentiment analysis doensn't keep up with the hyped promises.
See e.g.
The Sad State of Sentiment Analysis
December 26, 2013 by 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.
...
While the tools accurately predicted between 60 and 80% of utterances, when neutral utterances were removed (80% of the utterances) the accuracy dropped alarmingly.
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)
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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.
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.
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