Aspect-specific sentiment classification method based on high-dimensional representation

Hanqian Wu, Lu Cheng, Jie Li, Jingjing Wang, Jue Xie

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Aspect-specific sentiment classification is a fine grained sentiment classification task. The traditional coarse-grained sentiment analysis methods only identify the consumers’ sentiment polarity towards the product as a whole, but ignores the important attribute information, which leads to the inability to refine consumer preferences and clarify the advantages and disadvantages of commodity attributes. To solve this problem, we use the review text and its specific aspect information to construct a multi-level, high-dimensional deep neural network model. First, the clause segmentation algorithm is used to divide the review text into several clauses; secondly, the words in the clause are encoded by the bidirectional long short term memory neural network, and the vector representation of the clauses is obtained. Finally, the vector representation of the whole review text is obtained by using the bidirectional long short term memory neural network to code all the clauses. And then the sentiment polarity of review text is obtained by the softmax layer. The experimental result shows that the proposed method can effectively improve the performance of sentiment classification.
Original languageEnglish
Title of host publication3rd International Conference on Information Technology and Industrial Automation (ICITIA 2018)
EditorsShinfeng D. Lin, Abdul Hanan Abdullah, Wen-Tsai Sung
Place of PublicationLancaster PA USA
PublisherDEStech Publications, Inc
Pages183-190
Number of pages8
ISBN (Electronic)9781605956077
DOIs
Publication statusPublished - 2018
EventInternational Conference on Information Technology and Industrial Automation 2018 - Guangzhou, China
Duration: 21 Dec 201822 Dec 2018
Conference number: 3rd
http://www.dpi-proceedings.com/index.php/dtcse/issue/view/336/showToc (Proceedings)

Conference

ConferenceInternational Conference on Information Technology and Industrial Automation 2018
Abbreviated titleICITIA2018
CountryChina
CityGuangzhou
Period21/12/1822/12/18
Internet address

Keywords

  • Aspect-specific
  • Sentiment Classification
  • High-dimensional Text Representation
  • LSTM

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