Active learning support for targeted Twitter stream

About

The Twitter streaming API allows to track tweets about a specific topic by tracking user defined keywords. All tweets that contain a keyword can be accessed (as long as the volume is lower than 1% of total stream). However, tracking a topic via a keyword has two major disadvantages:

• Keywords allow for crude adjustments of precision / recall tradeoffs. In other words in many cases it has poor classification performance
• A researcher / user often does not think of all (or the most useful) keywords a priori.

This system is aimed to build a streaming interface that allows the user to obtain a fine tuned stream that maximizes the number of relevant tweets from the stream.

Given a set of user selected seed keywords, an initial stream is produced. The active learning component classifies tweets as relevant or not and concurrently presents tweets to the user for manual annotation. Only tweets that the system is most uncertain about are selected for manual annotation. A second component proposes new keywords based on co-occurence in the tweet text.

Dependencies

• See conda_environment.yml for all dependencies in the conda package manager format
• English language model for spaCy ($ python -m spacy download en)
• Mongodb (listening on localhost:27017 which is default setting when installing mongodb)

Run

Put your twitter credentials in a file named credentials.py of the following format:

credentials = {"coll_1": {
        "access_token": "...",
        "access_token_secret": "...",
        "consumer_secret": "...",
        "consumer_key": "...",
    }
}

Start the backend with:

python app.py

Monitor status with:

tail -f debug.log

Then open a browser and navigate to:

localhost:5000