{"_buckets": {"deposit": "5110c7d2-7e3b-4bb6-aed2-a47b209ce663"}, "_deposit": {"id": "18757", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "18757"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00018757"}, "item_10_alternative_title_19": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u8a00\u8a9e\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "A Proposal of Online Topic Model for Twitter : Considering Temporal Dynamics of User Interests and Topic Trends"}]}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-09", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "3", "bibliographicPageEnd": "6", "bibliographicPageStart": "1", "bibliographicVolumeNumber": "2013-MPS-95", "bibliographic_titles": [{"bibliographic_title": "\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c: MPS, \u6570\u7406\u30e2\u30c7\u30eb\u5316\u3068\u554f\u984c\u89e3\u6c7a\u7814\u7a76\u5831\u544a"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Latent Dirichlet Allocation (LDA) \u306f,\u69d8\u3005\u306a\u5206\u91ce\u3067\u5fdc\u7528\u3055\u308c\u3066\u3044\u308b\u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb\u3067\u3042\u308a,Twitter \u306b\u304a\u3051\u308b\u30e6\u30fc\u30b6\u5c5e\u6027\u306e\u63a8\u5b9a\u3084\u8a71\u984c\u306e\u8981\u7d04\u306a\u3069\u306b\u9069\u7528\u3057\u305f\u7814\u7a76\u3082\u6570\u591a\u304f\u5831\u544a\u3055\u308c\u59cb\u3081\u3066\u3044\u308b.LDA \u3092\u30c4\u30a4\u30fc\u30c8\u96c6\u5408\u306b\u9069\u7528\u3059\u308b\u5834\u5408,1 \u30c4\u30a4\u30fc\u30c8\u3092 1 \u6587\u66f8\u3068\u3059\u308b\u3068,\u6587\u66f8\u306e\u77ed\u3055\u3084\u30ce\u30a4\u30ba\u306e\u591a\u3055\u306b\u3088\u308a,LDA \u304c\u6709\u52b9\u306b\u6a5f\u80fd\u3057\u306a\u3044\u3053\u3068\u304c\u591a\u3044\u305f\u3081,1 \u30e6\u30fc\u30b6\u306e\u5168\u30c4\u30a4\u30fc\u30c8\u3092 1 \u6587\u66f8\u3068\u3059\u308b\u65b9\u6cd5\u304c\u4e00\u822c\u7684\u306b\u7528\u3044\u3089\u308c\u308b.\u3053\u308c\u306b\u5bfe\u3057\u3066,1 \u30c4\u30a4\u30fc\u30c8\u304c 1 \u30c8\u30d4\u30c3\u30af\u304b\u3089\u6210\u308b\u3068\u3044\u3046\u4eee\u5b9a\u306b\u57fa\u3065\u3044\u305f\u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb\u3067\u3042\u308b Twitter-LDA \u304c\u63d0\u6848\u3055\u308c,\u524d\u8005\u306e\u65b9\u6cd5\u306b\u6bd4\u3079\u3066,\u30c8\u30d4\u30c3\u30af\u306e\u610f\u5473\u306e\u307e\u3068\u307e\u308a\u306e\u9762\u3067\u512a\u308c\u3066\u3044\u308b\u3068\u5831\u544a\u3055\u308c\u3066\u3044\u308b.\u3057\u304b\u3057\u4e00\u65b9\u3067 Twitter-LDA \u306f,\u30aa\u30f3\u30e9\u30a4\u30f3\u5b66\u7fd2\u304c\u3067\u304d\u306a\u3044\u3068\u3044\u3046\u8ab2\u984c\u304c\u3042\u308b.\u672c\u8ad6\u6587\u3067\u306f,Twitter-LDA \u3092\u6539\u826f\u3057,Twitter \u306b\u9069\u3057\u305f\u30aa\u30f3\u30e9\u30a4\u30f3\u5b66\u7fd2\u53ef\u80fd\u306a\u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb\u3092\u63d0\u6848\u3059\u308b.\u63d0\u6848\u30e2\u30c7\u30eb\u3067\u306f\u4ee5\u4e0b\u306e\u4e8c\u70b9\u306b\u3064\u3044\u3066 Twitter-LDA \u3092\u62e1\u5f35\u3059\u308b.\u7b2c\u4e00\u306b,\u4e00\u822c\u8a9e\u3068\u30c8\u30d4\u30c3\u30af\u8a9e\u3068\u306e\u6bd4\u7387\u3092\u30e6\u30fc\u30b6\u3054\u3068\u306b\u63a8\u5b9a\u3059\u308b\u3053\u3068\u3067,\u3088\u308a\u9ad8\u7cbe\u5ea6\u306b\u30c4\u30a4\u30fc\u30c8\u306e\u751f\u6210\u904e\u7a0b\u3092\u30e2\u30c7\u30eb\u5316\u3059\u308b.\u7b2c\u4e8c\u306b,\u30e6\u30fc\u30b6\u306e\u8cfc\u8cb7\u884c\u52d5\u3092\u30e2\u30c7\u30eb\u5316\u3057\u305f Topic Tracking Model (TTM) \u306e\u6a5f\u69cb\u3092\u30e2\u30c7\u30eb\u306b\u52a0\u3048\u308b\u3053\u3068\u3067,Twitter \u306b\u304a\u3051\u308b\u30e6\u30fc\u30b6\u306e\u8208\u5473\u3068\u8a71\u984c\u306e\u6642\u9593\u767a\u5c55\u3092\u30aa\u30f3\u30e9\u30a4\u30f3\u3067\u5b66\u7fd2\u53ef\u80fd\u3068\u3059\u308b.Latent Dirichlet Allocation (LDA) is a topic model which has been applied to various fields. It has been also applied to user profiling or event summarization on Twitter. In the application of LDA to tweet collection, it generally treats aggregated all tweets of a user as a single document. On the other hand, Twitter-LDA which assumes a single tweet consists of a single topic has been proposed and showed that it is superior to the former way in topic semantic coherence. However, Twitter-LDA has a problem that it is not capable of online inference. In this paper, we extend Twitter-LDA in the following two points. First, we model the generation process of tweets more accurately by estimating the ratio between topic words and general words for each user. Second, we enable it to estimate temporal dynamics of user interests and topic trends in online based on Topic Tracking Model (TTM) which models consumer purchase behaviors.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://id.nii.ac.jp/1001/00095217/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/20853"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "\u4e00\u822c\u793e\u56e3\u6cd5\u4eba\u60c5\u5831\u51e6\u7406\u5b66\u4f1a"}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "\u3053\u3053\u306b\u63b2\u8f09\u3057\u305f\u8457\u4f5c\u7269\u306e\u5229\u7528\u306b\u95a2\u3059\u308b\u6ce8\u610f \u672c\u8457\u4f5c\u7269\u306e\u8457\u4f5c\u6a29\u306f\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u306b\u5e30\u5c5e\u3057\u307e\u3059\u3002\u672c\u8457\u4f5c\u7269\u306f\u8457\u4f5c\u6a29\u8005\u3067\u3042\u308b\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u306e\u8a31\u53ef\u306e\u3082\u3068\u306b\u63b2\u8f09\u3059\u308b\u3082\u306e\u3067\u3059\u3002\u3054\u5229\u7528\u306b\u5f53\u305f\u3063\u3066\u306f\u300c\u8457\u4f5c\u6a29\u6cd5\u300d\u306a\u3089\u3073\u306b\u300c\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u502b\u7406\u7db1\u9818\u300d\u306b\u5f93\u3046\u3053\u3068\u3092\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059\u3002Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). 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