Difference between revisions of "A Wikipedia-Based Semantic Relatedness Framework for Effective Dimensions Classification in Online Reputation Management"
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| + | | title = A Wikipedia-Based Semantic Relatedness Framework for Effective Dimensions Classification in Online Reputation Management | ||
| + | | date = 2018 | ||
| + | | authors = [[M. Atif Qureshi]]<br />[[Arjumand Younus]]<br />[[Colm O’Riordan]]<br />[[Gabriella Pasi]] | ||
| + | | doi = 10.1007/s12652-017-0536-y | ||
| + | | link = https://link.springer.com/article/10.1007/s12652-017-0536-y | ||
| + | }} | ||
'''A Wikipedia-Based Semantic Relatedness Framework for Effective Dimensions Classification in Online Reputation Management''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[M. Atif Qureshi]], [[Arjumand Younus]], [[Colm O’Riordan]] and [[Gabriella Pasi]]. | '''A Wikipedia-Based Semantic Relatedness Framework for Effective Dimensions Classification in Online Reputation Management''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[M. Atif Qureshi]], [[Arjumand Younus]], [[Colm O’Riordan]] and [[Gabriella Pasi]]. | ||
== Overview == | == Overview == | ||
Social media repositories serve as a significant source of evidence when extracting information related to the [[reputation]] of a particular entity (e.g., a particular politician, singer or company). Reputation management experts manually mine the social media repositories (in particular [[Twitter]]) for monitoring the reputation of a particular entity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computational methods for facilitating reputation management experts. A quite significant research challenge related to the above issue is to classify the reputation dimension of tweets with respect to entity names. More specifically, finding various aspects of a brand’s reputation is an important task which can help companies in monitoring areas of their strengths and weaknesses in an effective manner. To address this issue in this paper authors use dominant [[Wikipedia categories]] related to a reputation dimension; the dominant [[Wikipedia]] [[categories]] are then utilised within a semantic [[relatedness]] scoring framework to generate “associativities” with respect to the various reputation dimensions, and another version of “associativity” normalized by the “content entropy” of Wikipedia categories. The Wikipedia categories obtained through applied methods are finally used in a random forest classifier for the task of reputation dimensions classification. The experimental evaluations show a significant improvement over the baseline accuracy. | Social media repositories serve as a significant source of evidence when extracting information related to the [[reputation]] of a particular entity (e.g., a particular politician, singer or company). Reputation management experts manually mine the social media repositories (in particular [[Twitter]]) for monitoring the reputation of a particular entity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computational methods for facilitating reputation management experts. A quite significant research challenge related to the above issue is to classify the reputation dimension of tweets with respect to entity names. More specifically, finding various aspects of a brand’s reputation is an important task which can help companies in monitoring areas of their strengths and weaknesses in an effective manner. To address this issue in this paper authors use dominant [[Wikipedia categories]] related to a reputation dimension; the dominant [[Wikipedia]] [[categories]] are then utilised within a semantic [[relatedness]] scoring framework to generate “associativities” with respect to the various reputation dimensions, and another version of “associativity” normalized by the “content entropy” of Wikipedia categories. The Wikipedia categories obtained through applied methods are finally used in a random forest classifier for the task of reputation dimensions classification. The experimental evaluations show a significant improvement over the baseline accuracy. | ||
Revision as of 11:41, 15 September 2019
Authors | M. Atif Qureshi Arjumand Younus Colm O’Riordan Gabriella Pasi |
|---|---|
Publication date | 2018 |
DOI | 10.1007/s12652-017-0536-y |
Links | Original |
A Wikipedia-Based Semantic Relatedness Framework for Effective Dimensions Classification in Online Reputation Management - scientific work related to Wikipedia quality published in 2018, written by M. Atif Qureshi, Arjumand Younus, Colm O’Riordan and Gabriella Pasi.
Overview
Social media repositories serve as a significant source of evidence when extracting information related to the reputation of a particular entity (e.g., a particular politician, singer or company). Reputation management experts manually mine the social media repositories (in particular Twitter) for monitoring the reputation of a particular entity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computational methods for facilitating reputation management experts. A quite significant research challenge related to the above issue is to classify the reputation dimension of tweets with respect to entity names. More specifically, finding various aspects of a brand’s reputation is an important task which can help companies in monitoring areas of their strengths and weaknesses in an effective manner. To address this issue in this paper authors use dominant Wikipedia categories related to a reputation dimension; the dominant Wikipedia categories are then utilised within a semantic relatedness scoring framework to generate “associativities” with respect to the various reputation dimensions, and another version of “associativity” normalized by the “content entropy” of Wikipedia categories. The Wikipedia categories obtained through applied methods are finally used in a random forest classifier for the task of reputation dimensions classification. The experimental evaluations show a significant improvement over the baseline accuracy.
