Matches in SemOpenAlex for { <https://semopenalex.org/work/W2766705981> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W2766705981 abstract "Seeing the public of Bandung city as an active social media user, Bandung government provides channel in Twitter for citizen to report their complaints. In order to make the citizen complaint monitoring easier, there is a need to automatically detect the topics of complaint tweets (written in Indonesian language) in order to assist the government in managing the complaints reported. In this paper, a system to detect the topics of Indonesian complaint tweets automatically using supervised learning and unsupervised learning approaches is proposed. The supervised learning approach is implemented to classify complaint tweets topic, whereas the unsupervised learning approach is used to cluster complaint tweets based on the similarity of detail information contained in the complaints. Both the supervised learning and the unsupervised learning approaches are required to classify the topics of a tweet and to capture the detail information from each detected topic. The topics are classified using single label and multi label classification. The supervised learning approach is evaluated using accuracy, precision, recall, and F1 score. Three supervised machine learning algorithms are evaluated: Sequential Minimal Optimization, Naïve Bayes Multinomial, and Random Forests. The best algorithm for single label topic classification is SMO, with the accuracy average of 95%, whereas the best algorithm for multi-label topic classification is Random Forests, with 97.92% accuracy, 98.74% precision, 98.36% recall, and 98.44% F1 score. In the unsupervised learning approach, Clustering Index Value is used to evaluate the topic clusters detected. Two unsupervised learning algorithms are evaluated; Exemplar Based Topic Detection and Document Pivot Technique using TF-IDF. Exemplar Based Topic Detection has the best performance for detecting detail topic clusters with Clustering Index Value of 0.9653." @default.
- W2766705981 created "2017-11-10" @default.
- W2766705981 creator A5021128017 @default.
- W2766705981 creator A5022002234 @default.
- W2766705981 date "2017-08-01" @default.
- W2766705981 modified "2023-09-24" @default.
- W2766705981 title "Topic classification and clustering on Indonesian complaint tweets for bandung government using supervised and unsupervised learning" @default.
- W2766705981 cites W1496393218 @default.
- W2766705981 cites W1982029265 @default.
- W2766705981 cites W2137349054 @default.
- W2766705981 cites W2547291079 @default.
- W2766705981 cites W2562867853 @default.
- W2766705981 cites W2911964244 @default.
- W2766705981 cites W66588809 @default.
- W2766705981 doi "https://doi.org/10.1109/icaicta.2017.8090981" @default.
- W2766705981 hasPublicationYear "2017" @default.
- W2766705981 type Work @default.
- W2766705981 sameAs 2766705981 @default.
- W2766705981 citedByCount "3" @default.
- W2766705981 countsByYear W27667059812021 @default.
- W2766705981 countsByYear W27667059812023 @default.
- W2766705981 crossrefType "proceedings-article" @default.
- W2766705981 hasAuthorship W2766705981A5021128017 @default.
- W2766705981 hasAuthorship W2766705981A5022002234 @default.
- W2766705981 hasConcept C119857082 @default.
- W2766705981 hasConcept C12267149 @default.
- W2766705981 hasConcept C136389625 @default.
- W2766705981 hasConcept C148524875 @default.
- W2766705981 hasConcept C154945302 @default.
- W2766705981 hasConcept C169258074 @default.
- W2766705981 hasConcept C171686336 @default.
- W2766705981 hasConcept C17744445 @default.
- W2766705981 hasConcept C199539241 @default.
- W2766705981 hasConcept C204321447 @default.
- W2766705981 hasConcept C2780838233 @default.
- W2766705981 hasConcept C41008148 @default.
- W2766705981 hasConcept C50644808 @default.
- W2766705981 hasConcept C52001869 @default.
- W2766705981 hasConcept C58973888 @default.
- W2766705981 hasConcept C73555534 @default.
- W2766705981 hasConcept C8038995 @default.
- W2766705981 hasConcept C81669768 @default.
- W2766705981 hasConceptScore W2766705981C119857082 @default.
- W2766705981 hasConceptScore W2766705981C12267149 @default.
- W2766705981 hasConceptScore W2766705981C136389625 @default.
- W2766705981 hasConceptScore W2766705981C148524875 @default.
- W2766705981 hasConceptScore W2766705981C154945302 @default.
- W2766705981 hasConceptScore W2766705981C169258074 @default.
- W2766705981 hasConceptScore W2766705981C171686336 @default.
- W2766705981 hasConceptScore W2766705981C17744445 @default.
- W2766705981 hasConceptScore W2766705981C199539241 @default.
- W2766705981 hasConceptScore W2766705981C204321447 @default.
- W2766705981 hasConceptScore W2766705981C2780838233 @default.
- W2766705981 hasConceptScore W2766705981C41008148 @default.
- W2766705981 hasConceptScore W2766705981C50644808 @default.
- W2766705981 hasConceptScore W2766705981C52001869 @default.
- W2766705981 hasConceptScore W2766705981C58973888 @default.
- W2766705981 hasConceptScore W2766705981C73555534 @default.
- W2766705981 hasConceptScore W2766705981C8038995 @default.
- W2766705981 hasConceptScore W2766705981C81669768 @default.
- W2766705981 hasLocation W27667059811 @default.
- W2766705981 hasOpenAccess W2766705981 @default.
- W2766705981 hasPrimaryLocation W27667059811 @default.
- W2766705981 hasRelatedWork W2057245474 @default.
- W2766705981 hasRelatedWork W2940523548 @default.
- W2766705981 hasRelatedWork W3162567751 @default.
- W2766705981 hasRelatedWork W4285260836 @default.
- W2766705981 hasRelatedWork W4289347117 @default.
- W2766705981 hasRelatedWork W4297900598 @default.
- W2766705981 hasRelatedWork W4319309271 @default.
- W2766705981 hasRelatedWork W4360612004 @default.
- W2766705981 hasRelatedWork W4372080466 @default.
- W2766705981 hasRelatedWork W4381956280 @default.
- W2766705981 isParatext "false" @default.
- W2766705981 isRetracted "false" @default.
- W2766705981 magId "2766705981" @default.
- W2766705981 workType "article" @default.