Peringkasan Tweet Berdasarkan Trending Topic Twitter Dengan Pembobotan TF-IDF dan Single Linkage AngglomerativeHierarchical Clustering

Annisa Annisa, Yuda Munarko, Yufis Azhar


Trending topic is a feature provided by twitter that informs something widely discussed by users in a particular time. The form of a trending topic is a hashtag and can be selected by clicking. However, the number of tweets for each trending topics can be very large, so it will be difficult if we want to know all the contents. So, in order to make easy when reading the topic, a small number of tweets can be selected as the main idea of the topic. In this study, we applied the Agglomerative Single Linkage Hierarchical Clustering by calculating the TF-IDF value for each word in advance. We used 100 trending topics, where each topic consists of 50 tweets in Indonesian. For testing, we provided 30 trending topics which consist of 2 until 9 sub-topics. The result is that each trending topics can be summarized into shorter text contains 2 until 9 tweets. We were able to summarize 1 trending topics exactly same as the topic summarized by human expert. However, the rest of topics corresponded partially with human expert.


Text Summarization, TF-IDF, Single Linkage Agglomerative Hierarchical Clustering

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ISSN: 2503-2267