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Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!

Project description

Infer information from Tweets. Useful for human-centered computing tasks, such as sentiment analysis, location prediction, authorship profiling and more!

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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 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.

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infertweet-0.1.1.zip (34.1 kB view hashes)

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