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A wikipedia-based semantic relatedness framework for effective dimensions classification in online reputation management

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Abstract

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 we 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 our 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.

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Notes

  1. In the context of reputation management, an entity may refer to a celebrity, company, organization or brand.

  2. Note that these are the standard dimensions provided by the Reputation Institute.

  3. Available in 270+ languages.

  4. http://en.wikipedia.org/wiki/Wikipedia:Size_of_Wikipedia.

  5. http://en.wikipedia.org/wiki/Reliability_of_Wikipedia.

  6. Note that we have essentially utilised the dumps made available by DBPedia. However, despite the fact that DBPedia contains a notable work of semantic annotations, we are not using this additional information.

  7. musician1 and musician2 are two different musicians such as Madonna and Lady Gaga.

  8. Microsoft is a company whereas Windows10 is a product of Microsoft.

  9. It is this pre-defined entity corresponding to which reputation dimensions classification for the tweet has to be performed.

  10. An infobox is a fixed-format table designed to be added to the top right-hand corner of Wikipedia articles to consistently present a summary of some unifying aspect pertaining to the articles.

  11. It is important to note that a category representative of the entity is selected at this phase.

  12. An example category taxonomy for Apple Inc. can be seen on left side of Fig. 2.

  13. E.g., Wikipedia article “Steve Jobs” of “Apple Inc.” is mentioned inside a category “1955 births” which is not present either in parent nor in sub-categories of entity’s Wikipedia article.

  14. Normalizing a subtle relationship may result into mathematical zero due to small fraction and storing a low fraction with high precision is not an efficient choice.

  15. This could be a paragraph, sentence or tweet.

  16. Number of words in a phrase.

  17. Empirically this aggregation performs reasonably well during the evaluations as shown in the later chapters.

  18. Recall from Sect. 4.1.1 that the final step in extraction of candidate phrases corresponds to matching with Wikipedia article titles.

  19. From within training data.

  20. From the set WikiCategories that represents all Wikipedia categories within a given reputation dimension.

  21. Note that RD represents the set of all seven reputation dimensions.

  22. http://gephi.github.io.

  23. http://bit.ly/1eMADG9, we aim to release the API as an open source Wikipedia tool to facilitate other researchers.

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Authors and Affiliations

  1. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    M. Atif Qureshi & Arjumand Younus

  2. Information Technology Building, National University of Ireland, Galway, Ireland

    Colm O’Riordan

  3. Dipartimento di Informatica, Sistemistica e Comunicazione, Universita Degli Studi Di Milano-Bicocca, Milano, Italy

    Gabriella Pasi

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  1. M. Atif Qureshi
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  2. Arjumand Younus
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Correspondence to M. Atif Qureshi.

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Qureshi, M.A., Younus, A., O’Riordan, C. et al. A wikipedia-based semantic relatedness framework for effective dimensions classification in online reputation management. J Ambient Intell Human Comput 9, 1403–1413 (2018). https://doi.org/10.1007/s12652-017-0536-y

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