TY - GEN
T1 - Sentiment classification using TF-1DF features and extended space forest ensemble
AU - Cao, Nieqing
AU - Cao, Jingjing
AU - Lu, Haili
AU - Li, Bing
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/30
Y1 - 2015/11/30
N2 - With the rapid development of electronic commerce, user-generated contents have become increasingly important for customers and suppliers who want to get more feedback. They also have attracted a great deal of attention in the academics. Particularly, making sentiment classifications for these contents is significant. Till now, it has been proved that ensemble method is available for sentiment classification in the theory and practice. Following this direction we propose a new feature construction method by taking advantage of TF-IDF method, and an extended space forest ensemble method under the framework of bagging is employed for sentiment classification. In the experiment part, we make a performance comparison among the extended ensemble method with different feature operators and the original one based on two base classifiers by using public sentiment dataset. The empirical results show that the extended space forest ensemble method with appropriate feature operator can greatly improved the effectiveness of sentiment classification.
AB - With the rapid development of electronic commerce, user-generated contents have become increasingly important for customers and suppliers who want to get more feedback. They also have attracted a great deal of attention in the academics. Particularly, making sentiment classifications for these contents is significant. Till now, it has been proved that ensemble method is available for sentiment classification in the theory and practice. Following this direction we propose a new feature construction method by taking advantage of TF-IDF method, and an extended space forest ensemble method under the framework of bagging is employed for sentiment classification. In the experiment part, we make a performance comparison among the extended ensemble method with different feature operators and the original one based on two base classifiers by using public sentiment dataset. The empirical results show that the extended space forest ensemble method with appropriate feature operator can greatly improved the effectiveness of sentiment classification.
KW - Bagging
KW - Extended Space Forest
KW - Sentiment Classification
KW - TF-BJF
UR - http://www.scopus.com/inward/record.url?scp=85020751953&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2015.7340610
DO - 10.1109/ICMLC.2015.7340610
M3 - Conference Proceeding
AN - SCOPUS:85020751953
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
SP - 526
EP - 532
BT - Proceedings of 2015 International Conference on Machine Learning and Cybernetics, ICMLC 2015
PB - IEEE Computer Society
T2 - 14th International Conference on Machine Learning and Cybernetics, ICMLC 2015
Y2 - 12 July 2015 through 15 July 2015
ER -