Formal concept analysis for topic detection: a clustering quality experimental analysis
RepLab is a competitive evaluation exercise for Online Reputation Management systems organized as an activity of CLEF. RepLab 2013 focused on the task of monitoring the reputation of entities (companies, organizations, celebrities, etc.) on Twitter. The monitoring task for analysts consists of searc...
| Autores: | , , |
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| Tipo de documento: | conjunto de datos |
| Estado: | Versão publicada |
| Data de publicação: | 2021 |
| País: | España |
| Recursos: | Consorcio Madroño |
| Repositório: | e-cienciaDatos, Repositorio de Datos del Consorcio Madroño |
| OAI Identifier: | doi:10.21950/ML9OI9 |
| Acesso em linha: | https://doi.org/10.21950/ML9OI9 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Computer and Information Science Formal concept analysis Topic detection Clustering quality analysis Hierarchical agglomerative clustering Latent dirichlet allocation |
| Resumo: | RepLab is a competitive evaluation exercise for Online Reputation Management systems organized as an activity of CLEF. RepLab 2013 focused on the task of monitoring the reputation of entities (companies, organizations, celebrities, etc.) on Twitter. The monitoring task for analysts consists of searching the stream of tweets for potential mentions to the entity, filtering those that do refer to the entity, detecting topics (i.e., clustering tweets by subject) and ranking them based on the degree to which they signal reputation alerts (i.e., issues that may have a substantial impact on the reputation of the entity). The RepLab 2013 task is defined, accordingly, as (multilingual) topic detection combined with priority ranking of the topics, as input for reputation monitoring experts. The detection of reputational polarity (does the tweet have negative/positive implications for the reputation of the entity?) is an essential step to assign priority, and was evaluated as a standalone subtask |
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