Skip to content

Latest commit

 

History

History
110 lines (77 loc) · 4.39 KB

README.md

File metadata and controls

110 lines (77 loc) · 4.39 KB
Raw

Twitter Toolbox

A suite of tools for collecting, pre-processing, analyzing and sentiment-scoring twitter data. A additional brief walkthrough can be found here.

Install:

pip install twitter-nlp-toolkit

To utilize the sentiment analysis package, you will also need to install SpaCy's small English language model.

python -m spacy download en_core_web_sm

While the package is still under active development, the following functionality is expected to be stable:

Listener

twitter_nlp_toolkit.twitter_listener is the listener module, which can be used to monitor Twitter and stream tweets to disk in .json format.

keywords = ["python"]
stream = twitter_listener.TwitterStreamListener(**credentials)
stream.collect_from_stream(max_tweets=10,output_json_name="python_tweets.json", target_words=keywords)

"keywords" uses the Twitter API. Documentation and tips for setting up smart keyword queries can be found here

"credentials" contains your Twitter API key, which can be obtained for free here

The module also contains a parser to convert the .json-formatted tweets into .csv for easy use (ie, with Pandas) or convert straight to a pandas dataframe.

parser = tweet_json_parser.json_parser()
parser.stream_json_file(json_file_name="python_tweets.json",output_file_name="parsed_python_tweets.csv")

Alternatively, the parser can also convert the json file to a pandas dataframe

parser = tweet_json_parser.json_parser()
df = parser.parse_json_file_into_dataframe(json_file_name="ai_tweets.json")

Bulk Downloader

twitter_nlp_toolkit.twitter_REST_downloader is the bulk download module, which can be used to collect the last 200 (or so) tweets from a single user.

downloader = twitter_REST_downloader.bulk_downloader(**credentials)
downloader.get_tweets_csv_for_this_user("@nytimes","nyt_tweet_output.csv")

twitter_nlp_toolkit.tweet_sentiment_classifier is the sentiment analysis module, which can be used to classify the sentiment of tweets.

Classifier = tweet_sentiment_classifier.SentimentAnalyzer()
Classifier.load_small_ensemble()
Classifier.predict(['I am happy', 'I am sad', 'I am cheerful', 'I am mad']) # will return [1, 0, 1, 0]

Currently only two ensembles are provided: the small ensemble, which uses bag-of-words logistic regression model and two long short-term memory neural networks, and the large ensemble, which uses the bog-of-words model, two larger LSTM networks, and a Google BERT model. The large ensemble is more accurate (and expected to become much more accurate), but is extremely resource intensive and as such, isn't recommended for processing large numbers of tweets unless you have a powerful GPU.

These ensembles were trained primarily on the Sent140 dataset and primarily tested against the US Airlines dataset previously hosted on Crowdflower.com.

Please see the jupyter notebook (.ipynb) files at the root directory for further demonstrations of working code.

Advanced Use

If you have domain-specific training data, you can refine the ensembles:

Classifier.refine(train_x, train_y)
Classifier.save_models()
# To reload your saved models, you can run Classifier.load_models()

Other advanced use, such as building your own models, is possible but is not currently recommended as the models are still in development. Further documentation will be added once development stabilizes.

Bugs, issues, contributions, and feature requests

The developers are always open to feature requests, bugs reports, pull requests, and new opportunities to collaborate. Don't hesitate to reach out with questions, beedback, or requests.

Developers:

  • Moe Antar (@Moe520)

    • Twitter json parser and formatter
    • Twitter bulk downloader
    • Twitter Listener, in collaboration with Dr. Mirko Miorelli (https://github.com/mirkomiorelli)
    • File downloader
    • General maintenance and deployment procedures

  • Eric Schibli (@eschibli)

    • Data pre-processing pipeline
    • Natural language processing algorithms
    • Bag-of-words, LSTM neural networks, and BERT model assembly and training
    • General model optimization