Metadata-Version: 1.1
Name: infertweet
Version: 0.1.1
Summary: Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!
Home-page: http://www.github.com/bwbaugh/infertweet
Author: Wesley Baugh
Author-email: wesley@bwbaugh.com
License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License
Description: InferTweet
        ==========
        
        Infer information from Tweets. Useful for human-centered computing
        tasks, such as sentiment analysis, location prediction, authorship
        profiling and more!
        
        [![Build Status](https://travis-ci.org/bwbaugh/infertweet.png?branch=master)](https://travis-ci.org/bwbaugh/infertweet)
        
        Sentiment Analysis
        ------------------
        
        We provide three-class (positive, negative, objective-OR-neutral)
        sentiment analysis on tweets.
        
        Experiments are ongoing, but currently the system uses a hierarchical
        classifier that first determines if a tweet is objective or subjective
        (subjectivity classifier), and then if subjective determine if the tweet
        is positive or negative (polarity classifier).
        
        We use approximately 8,750 labeled training instances provided by the
        [Sentiment Analysis in Twitter](http://www.cs.york.ac.uk/semeval-2013/task2/)
        task for SemEval-2013. We then "freeze" the subjectivity classifier, as
        we currently haven't been able to incorporate additional high quality
        labeled or unlabeled objective-OR-neutral tweets or text. However, we
        continue to train the polarity classifier through self-training on
        approximately 1 million unlabeled tweets that are likely to contain
        sentiment. The additional tweets were captured from Twitter if they had
        a matching emoticon present in the text of the tweet.
        
        At the time of this writing, we are currently awaiting the results of
        our system in the SemEval-2013 competition. In the mean time, we have a
        lot more experimental ideas that may improve the performance of our
        classifiers!
        
        ### RPC server
        
        The sentiment analysis classifier can be loaded from file and served
        using a RPC server. This allows the classifier to potentially be used by
        many applications, as well as being able to stay loaded even if another
        application that depends on the classifier needs to restart or update.
        
        ### Web user interface
        
        We have added a very simple web interface that allows users to query the
        system. Lots of upcoming features are planned for the web interface.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing :: Linguistic
