BlogVox: Learning Sentiment Classifiers
Abstract
Performing sentiment analysis upon a topic, specified by key words, without prior knowledge about the key words is a difficult task. With the growth of the blogosphere researchers, corporations, and politicians, among others are very interested in applying sentiment detection to blogs. To accommodate the demands from myriad users, with similarly diverse desires, a sentiment analysis engine for blogs must discover domain specific features relevant to queries in order to accurately assess the sentiment of blogs. Using meta-learning upon the results of web searches, as BlogVox does, can accomplish this goal.
Cite
Text
Martineau et al. "BlogVox: Learning Sentiment Classifiers." AAAI Conference on Artificial Intelligence, 2007. doi:10.13016/m2zk55r01Markdown
[Martineau et al. "BlogVox: Learning Sentiment Classifiers." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/martineau2007aaai-blogvox/) doi:10.13016/m2zk55r01BibTeX
@inproceedings{martineau2007aaai-blogvox,
title = {{BlogVox: Learning Sentiment Classifiers}},
author = {Martineau, Justin and Java, Akshay and Kolari, Pranam and Finin, Timothy W. and Joshi, Anupam and Mayfield, James},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {1888-1889},
doi = {10.13016/m2zk55r01},
url = {https://mlanthology.org/aaai/2007/martineau2007aaai-blogvox/}
}