@inproceedings{WiegandKlakow2019, author = {Michael Wiegand and Dietrich Klakow}, title = {The Role of Knowledge-based Features in Polarity Classification at Sentence Level}, series = {Proceedings of the Twenty-Second International Florida Artificial Intelligence Research Society Conference, 19–21 May 2009, Sanibel Island, Florida, USA}, editor = {H. Chad Lane and Hans W. Guesgen}, publisher = {AAAI Press}, address = {Menlo Park, CA}, isbn = {978-1-57735-419-2}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84390}, pages = {296 -- 301}, year = {2019}, abstract = {Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy. In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.}, language = {en} }