TwitterHawk - a Twitter Sentiment Analysis System

TwitterHawk is an open-source natural language processing system for Twitter sentiment analysis. This system was developed for the SemEval-2015 Task 10: Sentiment Analysis in Twitter. The system placed 1st in topic-based sentiment classification (Subtask C), and ranked 6th out of 40 in identifying the sentiment of sarcastic tweets (for Subtask B). The system also placed 5th/11 for Subtask A (sentiment of a tweet’s sub-phrase), 10th/40 for Subtask B (sentiment of a full tweet), and 3rd/6 for Subtask D (summarization of Subtask C). TwitterHawk implements two classifiers, one for message-level sentiment, and one of phrase-level sentiment detection.


W. Boag P. Potash A. Rumshisky TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Association for Computational Linguistics. 2015

@inproceedings{boag_twitterhawk:_2015, author = "Boag, William and Potash, Peter and Rumshisky, Anna", address = "Denver, Colorado", title = "{TwitterHawk}: {A} {Feature} {Bucket} {Based} {Approach} to {Sentiment} {Analysis}", doi = "10.18653/v1/S15-2107", booktitle = "Proceedings of the 9th {International} {Workshop} on {Semantic} {Evaluation} ({SemEval} 2015)", publisher = "Association for Computational Linguistics", year = "2015", pages = "640--646" }