About Relevancer

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Relevancer aims at finding a balance between complexity of a tweet data set and expert needs from this data. The clustering step identify samples of the information groups that are present in the data set. The expert’s opinion is used to annotate selected clusters. As a result, the annotated clusters of tweets can be used as they are for further analysis and to build a classifier that can label remaining tweets, which were not included in the annotated clusters, and any other tweet set in light of the view of the expert. The process can be summarized as creating a need based classifier without sacrificing precision, information, and time. This process is fast enough to allow an expert to be able to repeat the process in case he/she decide to another type of annotation/analysis as he/she understand the data set better when he/she analyze first clusters. Relevancer uses as much as possible features and expert knowledge to allow basic and fast algorithms to be applied.

Usually, collections of tweets gathered by means of keyword-based queries are overly rich in the sense that not all tweets are relevant for a task at hand. Making sense of that data for a particular scenario depends on the context of the person who needs the information. Since nobody practically has time to read the whole of a tweet set, it is advisable to use machine learning to facilitate the first information organization steps. In practice, the most useful first step is to identify tweets that are clearly irrelevant for a particular task, for instance because they are posted by non-human accounts, contain spam, or point to another sense of a polysemous keyword used at query time. Available systems were designed for certain domains, languages, and use cases. Moreover each of them suffers from some combination of the following restrictions. First, they are restricted to analyzing tweets that contain at least a certain number of well-formed key terms or content words in certain languages. Second, they do not take into account tweet characteristics such as emoticons and personal language use. Third, they rarely use attributes of a tweet other than the text. Finally, they assume the availability of a set of annotators or a crowd that is willing to label a sufficient number of tweets. We developed our automatic Twitter filtering system Relevancer such that it does not suffer from the aforementioned restrictions. Relevancer facilitates exploring a tweet data set by dividing the data set into subsets to which the annotator can attach labels. Relevancer first employs an unsupervised clustering algorithm on a subset of the tweets to explore the structure of the tweet set, with explicit distinction of retweets and (near) duplicates. Subsequently, the annotator is asked to rate tweet clusters as being relevant and coherent. The clustering step is repeated for the remaining tweets until the domain expert stops the process, or a predefined target, such as the percentage of labeled tweets that should be labeled, is met. The type of clustering algorithm (currently, k- means and mini batch k-means are supported), and the annotation candidate group selection parameters are automatically adapted based on the size and the quality of the output respectively. The labeled tweet groups, the output of the unsupervised step, is used as training data for building automatic classifiers that can be applied ’in the wild’ to any new tweets gathered with the same query. The tool will be demonstrated with its web interface.

Relevancer was developed by Ali Hürriyetoğlu (@hurrial), Elif Türkay (@elif_turky), Mustafa Erkan Başar(@me_basar), Aslıhan Arslan(@miniminiibirkus), and Uğur Özcan(@uozcan12) as part of the ADNEXT (@Adnext_Commit) work package of the COMMIT/ Infiniti-project (@COMMIT_nl) project, with contributions from Nelleke Oostdijk, Antal van den Bosch (@antalvdb). the work of Elif Türkay, Aslıhan Arslan, and Uğur Özcan was made possible by the Erasmus programme.

Relevancer is being used in:
http://floodtags.com.webhosting109.transurl.nl/dengue-and-diarrhea-forecast/