Microblogs data management: a survey
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Cites background from "Microblogs data management: a surve..."
...Additionally, spatial keyword search is extensively investigated by existing studies [4], [7], [23], [24]....
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29 citations
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Cites background from "Microblogs data management: a surve..."
...[36] offer a comprehensive tutorial and survey, respectively, on this topic....
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References
3,976 citations
2,570 citations
"Microblogs data management: a surve..." refers methods in this paper
...The used models are both aggregation and SVM classification models....
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...ADSEM [242] uses SVM and naive Bayes classifiers to enhance the precision of mapping tweets to Wikipedia-based concepts....
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...It uses SVM classifiers and labeled training earthquake data to classify earthquake-related tweets....
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...The used features include different types of language-based features such as unigrams [5,35,120, 164,247,262], bigrams [35,120,262], trigrams [262], ngrams [82,180,185,248], and POS tags [5,39,120,180,185, 247,248,262], microblog-specific features [185,248] such as retweets [39], hashtags [35,39,164], emotions [35,39,164, 180], links [35,39], and other features such as punctuationbased [5,82,180,248], pattern-based [5,35,82,164,180,248] and semantic-based [180,247]....
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...The framework first applies transfer learning and label propagation to automatically generate labeled data, then learns an SVM text classifier based on tweet mini-clusters obtained by graph partitioning....
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1,652 citations
"Microblogs data management: a surve..." refers methods in this paper
...The used models are both aggregation and SVM classification models....
[...]
...ADSEM [242] uses SVM and naive Bayes classifiers to enhance the precision of mapping tweets to Wikipedia-based concepts....
[...]
...It uses SVM classifiers and labeled training earthquake data to classify earthquake-related tweets....
[...]
...The used features include different types of language-based features such as unigrams [5,35,120, 164,247,262], bigrams [35,120,262], trigrams [262], ngrams [82,180,185,248], and POS tags [5,39,120,180,185, 247,248,262], microblog-specific features [185,248] such as retweets [39], hashtags [35,39,164], emotions [35,39,164, 180], links [35,39], and other features such as punctuationbased [5,82,180,248], pattern-based [5,35,82,164,180,248] and semantic-based [180,247]....
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...The framework first applies transfer learning and label propagation to automatically generate labeled data, then learns an SVM text classifier based on tweet mini-clusters obtained by graph partitioning....
[...]
1,261 citations
"Microblogs data management: a surve..." refers methods in this paper
...The used features include different types of language-based features such as unigrams [5,35,120, 164,247,262], bigrams [35,120,262], trigrams [262], ngrams [82,180,185,248], and POS tags [5,39,120,180,185, 247,248,262], microblog-specific features [185,248] such as retweets [39], hashtags [35,39,164], emotions [35,39,164, 180], links [35,39], and other features such as punctuationbased [5,82,180,248], pattern-based [5,35,82,164,180,248] and semantic-based [180,247]....
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...In specific, SVM [79,136,266], NB [70,136], MNB [79], logistic regression [79,136,208], and AdaBoost [185] are still used, while new classifiers are also introduced such as neural models [86,136,363] and Bayes network [136]....
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...NB [70,136], MNB [79], logistic regression [79,136,208], and AdaBoost [185] are still used, while new classifiers are also introduced such as neural models [86,136,363] and...
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...The major used classifiers are support vector machines (SVM) [4,5,31,35,39,83,87,120,131,160, 164,172,180,248,262,275,306], (multinomial) naive Bayes (MNB and NB) [31,35,120,131,247,262], k-nearest neighbor (kNN) [31,82], MaxEnt [92,120,180], random forest (RF) [89,319], logistic regression [36,202,393], and AdaBoost [185]....
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...To enhance the classification accuracy, techniques of the second sub-category ensemble multiple classifiers [61,70, 75,79,86,136,172,185,194,208,244,266,317,363]....
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1,157 citations
"Microblogs data management: a surve..." refers background in this paper
...Existing deep learning techniques is exploited in short textual contexts in two-step fashion [37,62,71,86,90,134,147,148,165,265,280,288,321, 322,337,342,359]....
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