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https://issues.apache.org/jira/browse/OPENNLP-840?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15193860#comment-15193860
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Anastasija Mensikova commented on OPENNLP-840:
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Hello Mondher,
I’m currently a student at Trinity College, CT, majoring in Computer Science. I
have worked with sentiment analysis in the past, and even though I haven’t
written any complex algorithms, I have used them and written some simple ones
myself, and I am also currently taking a Coursera course on Machine Learning
and have already started working on the OpenNLP Sentiment Analysis
(https://github.com/amensiko/opennlp/tree/trunk/opennlp-tools/src/main/java/opennlp/tools/ml/sentiment).,
so I would really love to work on this kind of project! Throughout my college
career I have already learned various applicable computer science skills, so I
can learn any additional skills required very fast, and my passion for the
project will ensure the best possible outcome.
Another reason why I really want to take part in this project is that I want to
be a part of ASF and therefore learn more about it.
Could you please let me know how I can get the full document you provide in the
description of the project (http://www.ieice.org/ken/paper/20160129DbfF/eng/)?
I can’t seem to be able to download it. Also, where can I get the Twitter data
that I can use? Do you have it or is it still to be downloaded from the Twitter
API?
Thank you very much,
Anastasija
> 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] http://www.ieice.org/ken/paper/20160129DbfF/eng/
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