TY - GEN
T1 - Developing evaluation model of topical term for document-level sentiment classification
AU - Hu, Yi
AU - Li, Wenjie
AU - Lu, Qin
PY - 2008/12/1
Y1 - 2008/12/1
N2 - Sentiment classification is used to identify whether the opinion expressed in a document is positive or negative. In this paper, we present an evaluation modeling approach to document-level sentiment classification. The motivation of this work stems from the observation that the global document classification can benefit greatly by learning how a topical term is evaluated in its local sentence context. Two sentence-level sentiment evaluation models, namely positive and negative models, are constructed for each topical term. When analyzing a document, the evaluation models generate divergence to support sentence classification that in turn can be used to decide on the whole document classification collectively. When evaluated on a public available movie review corpus, the experimental results are comparable with the ones published. This is quite encouraging to us and motivates us to further investigate how to develop more effective evaluation models in the future.
AB - Sentiment classification is used to identify whether the opinion expressed in a document is positive or negative. In this paper, we present an evaluation modeling approach to document-level sentiment classification. The motivation of this work stems from the observation that the global document classification can benefit greatly by learning how a topical term is evaluated in its local sentence context. Two sentence-level sentiment evaluation models, namely positive and negative models, are constructed for each topical term. When analyzing a document, the evaluation models generate divergence to support sentence classification that in turn can be used to decide on the whole document classification collectively. When evaluated on a public available movie review corpus, the experimental results are comparable with the ones published. This is quite encouraging to us and motivates us to further investigate how to develop more effective evaluation models in the future.
KW - Evaluation model
KW - Maximum spanning tree
KW - Sentiment classification
KW - Topical term
UR - http://www.scopus.com/inward/record.url?scp=58349097805&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-89197-0_19
DO - 10.1007/978-3-540-89197-0_19
M3 - Conference article published in proceeding or book
SN - 354089196X
SN - 9783540891963
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 186
BT - PRICAI 2008
T2 - 10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
Y2 - 15 December 2008 through 19 December 2008
ER -