Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Classifying trending topics: a typology of conversation triggers on Twitter
Proceedings of the 20th ACM international conference on Information and knowledge management
Topic classification of blog posts using distant supervision
Proceedings of the Workshop on Semantic Analysis in Social Media
| Hi-index | 0.00 |
Classification is paramount for an optimal processing of tweets, albeit performance of classifiers is hindered by the need of large sets of training data to encompass the diversity of contents one can find on Twitter. In this paper, we introduce an inexpensive way of labeling large sets of tweets, which can be easily regenerated or updated when needed. We use human-edited web page directories to infer categories from URLs contained in tweets. By experimenting with a large set of more than 5 million tweets categorized accordingly, we show that our proposed model for tweet classification can achieve 82% in accuracy, performing only 12.2% worse than for web page classification.