Entry He:2012:ISP from talip.bib

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BibTeX entry

@Article{He:2012:ISP,
  author =       "Yulan He",
  title =        "Incorporating Sentiment Prior Knowledge for Weakly
                 Supervised Sentiment Analysis",
  journal =      j-TALIP,
  volume =       "11",
  number =       "2",
  pages =        "4:1--4:??",
  month =        jun,
  year =         "2012",
  DOI =          "https://doi.org/10.1145/2184436.2184437",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Tue Jun 12 11:20:16 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "This article presents two novel approaches for
                 incorporating sentiment prior knowledge into the topic
                 model for weakly supervised sentiment analysis where
                 sentiment labels are considered as topics. One is by
                 modifying the Dirichlet prior for topic-word
                 distribution (LDA-DP), the other is by augmenting the
                 model objective function through adding terms that
                 express preferences on expectations of sentiment labels
                 of the lexicon words using generalized expectation
                 criteria (LDA-GE). We conducted extensive experiments
                 on English movie review data and multi-domain sentiment
                 dataset as well as Chinese product reviews about mobile
                 phones, digital cameras, MP3 players, and monitors. The
                 results show that while both LDA-DP and LDA-GE perform
                 comparably to existing weakly supervised sentiment
                 classification algorithms, they are much simpler and
                 computationally efficient, rendering them more suitable
                 for online and real-time sentiment classification on
                 the Web. We observed that LDA-GE is more effective than
                 LDA-DP, suggesting that it should be preferred when
                 considering employing the topic model for sentiment
                 analysis. Moreover, both models are able to extract
                 highly domain-salient polarity words from text.",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing (TALIP)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

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