Improving Binary Classification on Text Problems using Differential Word Features
Links to Fileshttps://dl.acm.org/citation.cfm?id=1646291
MetadataShow full item record
Type of Work5 pages
conference papers and proceedings preprints
Citation of Original PublicationJustin Martineau, Tim Finin, Anupam Joshi, and Shamit Patel, Improving Binary Classification on Text Problems using Differential Word Features, Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009, DOI :10.1145/1645953.1646291
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natural language processing
support vector machine
support vector machines (SVM)
UMBC Ebiquity Research Group
We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference of its inverse document frequencies in the positive and negative training sets. While this technique is remarkably easy to implement, it gives a statistically significant improvement over the standard bag-of-words approaches using support vector machines on a range of classification tasks. Our results show that our technique is robust and broadly applicable. We provide an analysis of why the approach works and how it can generalize to other domains and problems.