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https://issues.apache.org/jira/browse/OPENNLP-840?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Mondher Bouazizi updated OPENNLP-840:
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Description:
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]
was:
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.
> Sentiment Analysis
> ------------------
>
> Key: OPENNLP-840
> URL: https://issues.apache.org/jira/browse/OPENNLP-840
> Project: OpenNLP
> Issue Type: New Feature
> Reporter: Mondher Bouazizi
> 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]
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