Formal concept analysis for topic detection: a clustering quality experimental analysis
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 h' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Clustering quality analysis","Formal concept analysis","Hierarchical agglomerative clustering","Latent dirichlet allocation","Topic detection"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 h' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 h
Clustering quality analysis; Formal concept analysis; Hierarchical agglomerative clustering; Latent dirichlet allocation; Topic detection