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  • 标题:Towards Mining Public Opinion: An Attention-Based Long Short Term Memory Network Using Transfer Learning
  • 本地全文:下载
  • 作者:G. M. Sakhawat Hossain ; Md. Harun Or Rashid ; Md. Rafiqul Islam
  • 期刊名称:Journal of Computer and Communications
  • 印刷版ISSN:2327-5219
  • 电子版ISSN:2327-5227
  • 出版年度:2022
  • 卷号:10
  • 期号:6
  • 页码:112-131
  • DOI:10.4236/jcc.2022.106010
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:The Internet provides a large number of tools and resources, such as social media sites, online newsgroups, blogs, electronic forums, virtual communities, and online travel sites, for consumers to express their views or opinions regarding various issues. These opinions can help organizations like tourism to improve their products and services for their consumers. Opinion mining refers to a process of identifying emotions by applying Natural Language Processing (NLP) techniques to a pool of texts. This paper mainly focuses on mining public opinion from the hotel reviews domain. To do so, we proposed a novel technique called the Attention-Based Long Short Term Memory (Attention-LSTM) Network using a transfer learning approach. We empirically analyzed several machine learning and deep learning methods and observed our proposed technique provided an adequate performance for mining public opinion in the hotel reviews domain.
  • 关键词:Opinion MiningDeep LearningWord2VecAttention-LSTMTransfer Learning
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