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Reputation analysis with a ranked sentiment-lexicon

Published: 03 July 2014 Publication History

Abstract

Reputation analysis is naturally linked to a sentiment analysis task of the targeted entities. This analysis leverages on a sentiment lexicon that includes general sentiment words and domain specific jargon. However, in most cases target entities are themselves part of the sentiment lexicon, creating a loop from which it is difficult to infer an entity reputation. Sometimes, the entity became a reference in the domain and is vastly cited as an example of a highly reputable entity. For example, in the movies domain it is not uncommon to see reviews citing Batman or Anthony Hopkins as esteemed references. In this paper we describe an unsupervised method for performing a simultaneous-analysis of the reputation of multiple named-entities. Our method jointly extracts named entities reputation and a domain specific sentiment lexicon. The objective is two-fold: (1) named-entities are naturally ranked by our method and (2) we can build a reputation graph of the domain's named entities. This framework has immediate applications in terms of visualization or search by reputation.

References

[1]
Chen, L. et al. Extracting Diverse Sentiment Expressions with Target-Dependent Polarity from Twitter. ICWSM,2012.
[2]
Esuli, A. and Sebastiani, F. Sentiwordnet: A publicly available lexical resource for opinion mining. In LREC'06,2006.
[3]
Go, A. et al. Twitter Sentiment Classification using Distant Supervision. Technical report, Stanford, 2009.
[4]
Hatzivassiloglou, V. and McKeown, K.R. Predicting the semantic orientation of adjectives. ACL,1997.
[5]
Joshi, M. et al. 2010. Movie Reviews and Revenues: An Experiment in Text Regression. HLT '10: NAACL, 2010.
[6]
Martín-Wanton, T. et al. An unsupervised transfer learning approach to discover topics for online reputation management. CIKM'13,2013.
[7]
Martín-Wanton, T. et al. UNED at RepLab 2012: Monitoring Task. CLEF, 2012.
[8]
Oghina, A. et al. Predicting IMDB Movie Ratings Using Social Media. ECIR'2012,2012.
[9]
Pang, B. and Lee, L. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. ACL,2005.
[10]
Spina, D. et al. UNED Online Reputation Monitoring Team at RepLab 2013. CLEF, 2013.
[11]
Turney, P. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. ACL '02,2002.
[12]
Turney, P.D. and Littman, M.L. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, TOIS,2003.
[13]
Villena-Román, J. et al. DAEDALUS at RepLab 2012: Polarity Classification and Filtering on Twitter Data. CLEF,2012.
[14]
Wiebe, J. and Cardie, C. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation. ACH, 2005.

Cited By

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  • (2017)Extending Various Thesauri by Finding Synonym Sets from a Formal Concept LatticeJournal of Natural Language Processing10.5715/jnlp.24.32324:3(323-349)Online publication date: 2017
  • (2015)Build Emotion Lexicon from Microblogs by Combining Effects of Seed Words and Emoticons in a Heterogeneous GraphProceedings of the 26th ACM Conference on Hypertext & Social Media10.1145/2700171.2791035(283-292)Online publication date: 24-Aug-2015
  • (2015)Multi-dimensional Reputation Modeling Using Micro-blog ContentsFoundations of Intelligent Systems10.1007/978-3-319-25252-0_48(452-457)Online publication date: 30-Dec-2015

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  1. Reputation analysis with a ranked sentiment-lexicon

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    cover image ACM Conferences
    SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
    July 2014
    1330 pages
    ISBN:9781450322577
    DOI:10.1145/2600428
    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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    Publication History

    Published: 03 July 2014

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    Author Tags

    1. lda
    2. reputation analysis
    3. sentiment lexicons

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    View all
    • (2017)Extending Various Thesauri by Finding Synonym Sets from a Formal Concept LatticeJournal of Natural Language Processing10.5715/jnlp.24.32324:3(323-349)Online publication date: 2017
    • (2015)Build Emotion Lexicon from Microblogs by Combining Effects of Seed Words and Emoticons in a Heterogeneous GraphProceedings of the 26th ACM Conference on Hypertext & Social Media10.1145/2700171.2791035(283-292)Online publication date: 24-Aug-2015
    • (2015)Multi-dimensional Reputation Modeling Using Micro-blog ContentsFoundations of Intelligent Systems10.1007/978-3-319-25252-0_48(452-457)Online publication date: 30-Dec-2015

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