TY - GEN
T1 - A linear-chain CRF-based learning approach for web opinion mining
AU - Qi, Luole
AU - Chen, Li
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - The task of opinion mining from product reviews is to extract the product entities and determine whether the opinions on the entities are positive, negative or neutral. Reasonable performance on this task has been achieved by employing rule-based, statistical approaches or generative learning models such as hidden Markov model (HMMs). In this paper, we proposed a discriminative model using linear-chain Conditional Random Field (CRFs) for opinion mining. CRFs can naturally incorporate arbitrary, non-independent features of the input without making conditional independence assumptions among the features. This can be particularly important for opinion mining on product reviews. We evaluated our approach base on three criteria: recall, precision and F-score for extracted entities, opinions and their polarities. Compared to other methods, our approach was proven more effective for accomplishing opinion mining tasks.
AB - The task of opinion mining from product reviews is to extract the product entities and determine whether the opinions on the entities are positive, negative or neutral. Reasonable performance on this task has been achieved by employing rule-based, statistical approaches or generative learning models such as hidden Markov model (HMMs). In this paper, we proposed a discriminative model using linear-chain Conditional Random Field (CRFs) for opinion mining. CRFs can naturally incorporate arbitrary, non-independent features of the input without making conditional independence assumptions among the features. This can be particularly important for opinion mining on product reviews. We evaluated our approach base on three criteria: recall, precision and F-score for extracted entities, opinions and their polarities. Compared to other methods, our approach was proven more effective for accomplishing opinion mining tasks.
KW - Conditional Random Field (CRFs)
KW - Feature Function
KW - Web Opinion Mining
UR - http://www.scopus.com/inward/record.url?scp=78751534881&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17616-6_13
DO - 10.1007/978-3-642-17616-6_13
M3 - Conference proceeding
AN - SCOPUS:78751534881
SN - 3642176151
SN - 9783642176159
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 128
EP - 141
BT - Web Information Systems Engineering, WISE 2010 - 11th International Conference, Proceedings
T2 - 11th International Conference on Web Information Systems Engineering, WISE 2010
Y2 - 12 December 2010 through 14 December 2010
ER -