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LCI: a social channel analysis platform for live customer intelligence

Published:12 June 2011Publication History

ABSTRACT

The rise of Web 2.0 with its increasingly popular social sites like Twitter, Facebook, blogs and review sites has motivated people to express their opinions publicly and more frequently than ever before. This has fueled the emerging field known as sentiment analysis whose goal is to translate the vagaries of human emotion into hard data. LCI is a social channel analysis platform that taps into what is being said to understand the sentiment with the particular ability of doing so in near real-time. LCI integrates novel algorithms for sentiment analysis and a configurable dashboard with different kinds of charts including dynamic ones that change as new data is ingested. LCI has been researched and prototyped at HP Labs in close interaction with the Business Intelligence Solutions (BIS) Division and a few customers. This paper presents an overview of the architecture and some of its key components and algorithms, focusing in particular on how LCI deals with Twitter and illustrating its capabilities with selected use cases.

References

  1. X.Ding, B.Liu and P.Yu. A Holistic Lexicon-Based Approach to Opinion Mining. In Proc. WSDM 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. B.Pang, L.Lee and S.Vaithyananthan. Thumbs up? Sentiment classification using machine learning techniques. In Proc. EMNLP 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Q.Guang, B.Liu., J.Bu and C.Chen. Expanding Domain Sentiment Lexicon through Double Propagation. In Proc. IJCAI 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D.Xiaowen.,B.Liu and L.Zhang. Entity discovery and assignment for opinion mining applications. In proc. KDD 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J.Shawe-Taylor, N.Cristianini. Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Y.Lu, M.Castellanos, U.Dayal and C.Zhai. Automatic Construction of a Context-Aware Sentiment Lexicon: An Optimization Approach. In Proc. WWW 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Y. Lu, C. Zhai, and N. Sundaresan. Rated aspect summarization of short comments. In 18th International World Wide Web Conference (WWW2009), April 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
      June 2011
      1364 pages
      ISBN:9781450306614
      DOI:10.1145/1989323

      Copyright © 2011 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 June 2011

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