[jira] [Commented] (OPENNLP-840) Sentiment Analysis

["Chris A. Mattmann (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/OPENNLP-840?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16004974#comment-16004974 ] 

Chris A. Mattmann commented on OPENNLP-840:
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[~joern] yes I just haven't had time, but I would appreciate if possible the issue being left open - I do intend to fix it when I get the time.

> 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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