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https://issues.apache.org/jira/browse/OPENNLP-840?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16064100#comment-16064100
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Chris A. Mattmann commented on OPENNLP-840:
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Resolved thanks [[email protected]] and [~joern] all that
contributed!
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remote: opennlp git commit: Merge branch 'master' of
https://github.com/apache/opennlp
remote: opennlp git commit: Remove HT models, not part of Apache distribution.
remote: opennlp git commit: Merge branch 'OPENNLP-840-2' of
https://github.com/amensiko/opennlp
remote: opennlp git commit: Adding sentiment analysis code to OpenNLP:
OPENNLP-840
To https://git-wip-us.apache.org/repos/asf/opennlp.git
0201285..e515ff4 master -> master
Branch master set up to track remote branch master from apache.
LMC-053601:opennlp mattmann$
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> 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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