research-article

LCI: a social channel analysis platform for live customer intelligence

Authors:

Yue Lu,

Mark SchreimanAuthors Info & Claims

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

Pages 1049 - 1058

https://doi.org/10.1145/1989323.1989436

Published: 12 June 2011 Publication History

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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.

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Cited By

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Hua YDenny PWicker JTaskova K(2024)A systematic review of aspect-based sentiment analysis: domains, methods, and trendsArtificial Intelligence Review10.1007/s10462-024-10906-z57:11Online publication date: 17-Sep-2024
https://doi.org/10.1007/s10462-024-10906-z
Panwar ABhatnagar V(2022)Sentiment Analysis of Game Review Using Machine Learning in a Hadoop EcosystemResearch Anthology on Implementing Sentiment Analysis Across Multiple Disciplines10.4018/978-1-6684-6303-1.ch026(463-483)Online publication date: 10-Jun-2022
https://doi.org/10.4018/978-1-6684-6303-1.ch026
Chee TWu EWu H(2022)Sensible Tweets: Speech Act Filtering for Effective Sentiment Analysis2022 6th International Conference on Business and Information Management (ICBIM)10.1109/ICBIM57406.2022.00038(173-177)Online publication date: 26-Aug-2022
https://doi.org/10.1109/ICBIM57406.2022.00038
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Index Terms

LCI: a social channel analysis platform for live customer intelligence
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Information & Contributors

Information

Published In

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

June 2011

1364 pages

ISBN:9781450306614

DOI:10.1145/1989323

General Chair:
Timos Sellis
IMIS/RC Athena
,
Program Chair:
Renée J. Miller
University of Toronto
,
Publications Chairs:
Anastasios Kementsietsidis
IBM T.J. Watson Research Center
,
Yannis Velegrakis
University of Trento

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2011

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SIGMOD/PODS '11

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SIGMOD

SIGMOD/PODS '11: International Conference on Management of Data

June 12 - 16, 2011

Athens, Greece

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Hua YDenny PWicker JTaskova K(2024)A systematic review of aspect-based sentiment analysis: domains, methods, and trendsArtificial Intelligence Review10.1007/s10462-024-10906-z57:11Online publication date: 17-Sep-2024
https://doi.org/10.1007/s10462-024-10906-z
Panwar ABhatnagar V(2022)Sentiment Analysis of Game Review Using Machine Learning in a Hadoop EcosystemResearch Anthology on Implementing Sentiment Analysis Across Multiple Disciplines10.4018/978-1-6684-6303-1.ch026(463-483)Online publication date: 10-Jun-2022
https://doi.org/10.4018/978-1-6684-6303-1.ch026
Chee TWu EWu H(2022)Sensible Tweets: Speech Act Filtering for Effective Sentiment Analysis2022 6th International Conference on Business and Information Management (ICBIM)10.1109/ICBIM57406.2022.00038(173-177)Online publication date: 26-Aug-2022
https://doi.org/10.1109/ICBIM57406.2022.00038
Abdella JZaki NShuaib KKhan F(2021)Airline ticket price and demand prediction: A surveyJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2019.02.00133:4(375-391)Online publication date: May-2021
https://doi.org/10.1016/j.jksuci.2019.02.001
Panwar ABhatnagar V(2020)Sentiment Analysis of Game Review Using Machine Learning in a Hadoop EcosystemHandbook of Research on Engineering Innovations and Technology Management in Organizations10.4018/978-1-7998-2772-6.ch008(145-165)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2772-6.ch008
Nedjah NSantos Ide Macedo Mourelle L(2019)Sentiment analysis using convolutional neural network via word embeddingsEvolutionary Intelligence10.1007/s12065-019-00227-415:4(2295-2319)Online publication date: 3-Apr-2019
https://doi.org/10.1007/s12065-019-00227-4
Chaudhuri AChaudhuri A(2019)IntroductionVisual and Text Sentiment Analysis through Hierarchical Deep Learning Networks10.1007/978-981-13-7474-6_1(1-8)Online publication date: 7-Apr-2019
https://doi.org/10.1007/978-981-13-7474-6_1
BalaAnand MKarthikeyan NKarthik S(2018)RETRACTED ARTICLE: Designing a Framework for Communal Software: Based on the Assessment Using Relation ModellingInternational Journal of Parallel Programming10.1007/s10766-018-0598-248:2(329-343)Online publication date: 29-Aug-2018
https://doi.org/10.1007/s10766-018-0598-2
Ndong JGueye I(2018)A Robust Process to Identify Pivots Inside Sub-communities in Social NetworksInnovations and Interdisciplinary Solutions for Underserved Areas10.1007/978-3-319-98878-8_23(248-260)Online publication date: 30-Aug-2018
https://doi.org/10.1007/978-3-319-98878-8_23
Jiménez-Zafra SMartín-Valdivia MMartínez-Cámara EUreña-López L(2016)Combining resources to improve unsupervised sentiment analysis at aspect-levelJournal of Information Science10.1177/016555151559368642:2(213-229)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1177/0165551515593686
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