TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Microblog User Geolocation by Extracting Local Words Based on Word Clustering and Wrapper Feature Selection |
| 영문제목(English Title) |
Microblog User Geolocation by Extracting Local Words Based on Word Clustering and Wrapper Feature Selection |
| 저자(Author) |
Hechan Tian
Fenlin Liu
Xiangyang Luo
Fan Zhang
Yaqiong Qiao
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| 원문수록처(Citation) |
VOL 14 NO. 10 PP. 3972 ~ 3988 (2020. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.
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| 키워드(Keyword) |
Location Prediction
Word Clustering
Feature Selection
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| 파일첨부 |
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