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https://issues.apache.org/jira/browse/OPENNLP-840?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16064092#comment-16064092
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Chris A. Mattmann commented on OPENNLP-840:
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OK all tests pass and build works:
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[INFO] Executed tasks
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[INFO] --- maven-install-plugin:2.5.2:install (default-install) @ opennlp-distr
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[INFO] Installing
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[INFO] ------------------------------------------------------------------------
[INFO] Reactor Summary:
[INFO]
[INFO] Apache OpenNLP Reactor ............................. SUCCESS [ 4.749 s]
[INFO] Apache OpenNLP Tools ............................... SUCCESS [01:07 min]
[INFO] Apache OpenNLP UIMA Annotators ..................... SUCCESS [ 10.972 s]
[INFO] Apache OpenNLP Brat Annotator ...................... SUCCESS [ 11.016 s]
[INFO] Apache OpenNLP Morfologik Addon .................... SUCCESS [ 5.650 s]
[INFO] Apache OpenNLP Documentation ....................... SUCCESS [ 9.800 s]
[INFO] Apache OpenNLP Distribution ........................ SUCCESS [ 4.986 s]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 01:55 min
[INFO] Finished at: 2017-06-25T07:14:38-07:00
[INFO] Final Memory: 79M/1658M
[INFO] ------------------------------------------------------------------------
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Going to commit now!
> Sentiment Analysis
> ------------------
>
> Key: OPENNLP-840
> URL: https://issues.apache.org/jira/browse/OPENNLP-840
> Project: OpenNLP
> Issue Type: New Feature
> Reporter: Mondher Bouazizi
> Assignee: Chris A. Mattmann
> Labels: gsoc, gsoc2016, nlp
>
> The objective of the "Sentiment Analysis" component is to determine the
> sentiment of the author towards the object of his text.
> Different techniques are proposed in the academic literature, and some state
> of the art approaches present very high accuracy.
> Sentiment analysis can have different granularity levels:
> - Binary classification: in this case, the text is to be classified into two
> classes which are "positive" and "negative".
> - Ternary classification: in addition to the two classes present in the
> binary classification, a third class is added which is "neutral".
> - Multi-class sentiment analysis: the two classes "positive" and "negative"
> are further divided into sub-classes (e.g., "love" happiness", etc. for the
> positive class; and "hate", "anger", etc. for the negative class). Therefore
> the classification objective is to determine the sentiment sub-class instead
> of the main polarity
> In this component, we will implement some of the state of the art approaches,
> in particular the one presented here[1]. approaches use machine-learning
> techniques to learn a classifier from labeled training sets.
> -----------------------------------------------
> [1] http://www.ieice.org/ken/paper/20160129DbfF/eng/
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