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Chris A. Mattmann commented on OPENNLP-840: ------------------------------------------- [~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/ -- This message was sent by Atlassian JIRA (v6.3.15#6346)