• DocumentCode
    3579778
  • Title

    Reviews Analysis Based on Sentence and Word Relevance

  • Author

    Shibo Zhang ; Yun Sha ; Xiaojie Wang

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Beijing Univ. of Posts & Telecommun., Beijing, China
  • Volume
    1
  • fYear
    2014
  • Firstpage
    43
  • Lastpage
    46
  • Abstract
    Assuming that one sentence in review expresses one opinion, LDA based on sentence is performed to analysis the massive online reviews. When computing the topic´s word, word relevance measure is designed which penalizes the word frequency by a factor that captures how much the word is shared across topics, words for topics can been selected more accurately. Experiments on massive review crawled from network show that the result of analyzing is better than the standard LDA, there is clearer topic cue, and recognition is improved among the topics.
  • Keywords
    Internet; natural language processing; LDA; latent Dirichlet allocation; online reviews; reviews analysis; sentence; word frequency; word relevance; word relevance measure; Analytical models; Computational modeling; Entropy; Frequency conversion; Frequency measurement; Resource management; Standards; latent dirichlet allocation model; online reviews; topic analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
  • Print_ISBN
    978-1-4799-7004-9
  • Type

    conf

  • DOI
    10.1109/ISCID.2014.21
  • Filename
    7064135