Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.754
Building a model using machine learning that can classify the sentiment of natural language text often requires an extensive set of labeled training data from the same domain as the target text. Gathering and labeling new datasets whenever a model is needed for a new domain is time-consuming and difficult, especially if a dataset with numeric ratings is not available. In this paper we consider the problem of building models that have a high sentiment classification accuracy without the aid of a labeled dataset from the target domain. We show that ensembles of existing domain models can be used to achieve a classification accuracy that approaches that of models trained on data from the target domain.
Machine Learning, Data Mining, Sentiment Mining, Opinion Mining
Matthew Whitehead, Larry Yaeger, "Building a General Purpose Cross-Domain Sentiment Mining Model", CSIE, 2009, Computer Science and Information Engineering, World Congress on, Computer Science and Information Engineering, World Congress on 2009, pp. 472-476, doi:10.1109/CSIE.2009.754