Difference between revisions of "Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet"
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| − | '''Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet''' - scientific work related to Wikipedia quality published in 2014, written by Muhammad Atif Qureshi, Arjumand Younus, Colm O'Riordan and Gabriella Pasi. | + | '''Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Muhammad Atif Qureshi]], [[Arjumand Younus]], [[Colm O'Riordan]] and [[Gabriella Pasi]]. |
== Overview == | == Overview == | ||
| − | Social media repositories serve as a significant source of ev- idence when extracting information related to the reputation of a par- ticular entity (e.g., a particular politician, singer or company). Reputa- tion management experts manually mine the social media repositories (in particular Twitter) for monitoring the reputation of a particular en- tity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computa- tional 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 impor- tant 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 in a random forest classifier. Additionally authors also use tweet-specific fea- tures, language-specific features and similarity-based features. The ex- perimental evaluations show a significant improvement over the baseline accuracy. | + | Social media repositories serve as a significant source of ev- idence when extracting information related to the [[reputation]] of a par- ticular entity (e.g., a particular politician, singer or company). Reputa- tion management experts manually mine the social media repositories (in particular [[Twitter]]) for monitoring the reputation of a particular en- tity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computa- tional 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 impor- tant 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 in a random forest classifier. Additionally authors also use tweet-specific fea- tures, language-specific [[features]] and similarity-based features. The ex- perimental evaluations show a significant improvement over the baseline accuracy. |
Revision as of 12:52, 3 February 2020
Cirgirgdisco at Replab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet - scientific work related to Wikipedia quality published in 2014, written by Muhammad Atif Qureshi, Arjumand Younus, Colm O'Riordan and Gabriella Pasi.
Overview
Social media repositories serve as a significant source of ev- idence when extracting information related to the reputation of a par- ticular entity (e.g., a particular politician, singer or company). Reputa- tion management experts manually mine the social media repositories (in particular Twitter) for monitoring the reputation of a particular en- tity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computa- tional 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 impor- tant 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 in a random forest classifier. Additionally authors also use tweet-specific fea- tures, language-specific features and similarity-based features. The ex- perimental evaluations show a significant improvement over the baseline accuracy.
