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Sentiment recognition of online course reviews using multi-swarm optimization-based selected features

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成果类型:
期刊论文
作者:
Liu, Zhi;Liu, Sanya*;Liu, Lin;Sun, Jianwen(孙建文);Peng, Xian;...
通讯作者:
Liu, Sanya
作者机构:
[Sun, Jianwen; Liu, Zhi; Wang, Tai; Peng, Xian; Liu, Sanya; Liu, Lin] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构:
[Liu, Sanya] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Course reviews;Ensemble feature selection;Multi-swarm particle swarm optimization (MSPSO);Sentiment discriminability;Sentiment recognition
期刊:
Neurocomputing
ISSN:
0925-2312
年:
2016
卷:
185
页码:
11-20
基金类别:
National Social Science Fund Project of China [14BGL131]; Humanity and Social Science Youth foundation of Ministry of Education of China [15YJC880088]; self-determined research funds of CCNU from the colleges' basic research and operation of MOE [CCNU15A05009, CCNU15A05010, CCNU15GF001, CCNU15A06072]
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Sentiment recognition of online course reviews is valuable to understand emotions and feelings of learners. Nowadays, an increasing number of course reviews are being generated with the emergence of Massive Open Online Courses (MOOCs), which offers teachers a chance to analyze the opinions of learners and improve teaching strategies. However, the unstructured data contain large amounts of redundant features, which will significantly impact the performance of machine learning. To select effective emotional features, we adopt a multi-swarm particle swarm optimization (MSPSO) method, which genera...

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