Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Purpose#R##N##R##N##R##N##R##N##R##N#Many Twitter users post tets that are related to their particular interests. Users can also collect information by following other users. One approach clarifies user interests by tagging labels based on the users. A user tagging method is important to discover candidate users with similar interests.  aims to propose a new user tagging method using the posting time series data of the number of tets.#R##N##R##N##R##N##R##N##R##N#Design/methodology/approach#R##N##R##N##R##N##R##N##R##N#Our hypothesis focuses on the relationship beten a user’s interests and the posting times of tets: as users have interests, they will post more tets at the time when events occur compared with general times. The authors assume that hashtags are labeled tags to users and observe their occurrence counts in each timestamp. The authors extract burst timestamps using Kleinberg’s burst enumeration algorithm and estimate the burst levels. The authors manage the burst levels as term frequency in documents and calculate the score using typical methods such as cosine similarity, Naive Bayes and term frequency (TF) in a document and inversed document frequency (IDF; TF-IDF).#R##N##R##N##R##N##R##N##R##N#Findings#R##N##R##N##R##N##R##N##R##N#From the sophisticated experimental evaluations, the authors demonstrate the high efficiency of the tagging method. Naive Bayes and cosine similarity are particular suitable for the user tagging and tag score calculation tasks, respectively. Some users, whose hashtags re appropriately estimated by our methods, experienced higher the maximum value of the number of tets than other users.#R##N##R##N##R##N##R##N##R##N#Originality/value#R##N##R##N##R##N##R##N##R##N#Many approaches estimate user interest based on the terms in tets and apply such graph theory as following networks. The authors propose a new estimation method that uses the time series data of the number of tets. The merits to estimating user interest using the time series data do not depend on language and can decrease the calculation costs compared with the above-mentioned approaches because the number of features is fer.
['Shuhei Yamamoto', 'Kei Wakabayashi', 'Noriko Kando', 'Tetsuji Satoh']
I am writing a research paper on a idea Twitter user tagging method based on burst time series
[{"from": "human", "value": "I am writing a research paper on a idea Twitter user tagging method based on burst time series"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Purpose#R##N##R##N##R##N##R##N##R##N#Many Twitter users post tets that are related to their particular interests. Users can also collect information by following other users. One approach clarifies user interests by tagging labels based on the users. A user tagging method is important to discover candidate users with similar interests.  aims to propose a new user tagging method using the posting time series data of the number of tets.#R##N##R##N##R##N##R##N##R##N#Design/methodology/approach#R##N##R##N##R##N##R##N##R##N#Our hypothesis focuses on the relationship beten a user\u2019s interests and the posting times of tets: as users have interests, they will post more tets at the time when events occur compared with general times. The authors assume that hashtags are labeled tags to users and observe their occurrence counts in each timestamp. The authors extract burst timestamps using Kleinberg\u2019s burst enumeration algorithm and estimate the burst levels. The authors manage the burst levels as term frequency in documents and calculate the score using typical methods such as cosine similarity, Naive Bayes and term frequency (TF) in a document and inversed document frequency (IDF; TF-IDF).#R##N##R##N##R##N##R##N##R##N#Findings#R##N##R##N##R##N##R##N##R##N#From the sophisticated experimental evaluations, the authors demonstrate the high efficiency of the tagging method. Naive Bayes and cosine similarity are particular suitable for the user tagging and tag score calculation tasks, respectively. Some users, whose hashtags re appropriately estimated by our methods, experienced higher the maximum value of the number of tets than other users.#R##N##R##N##R##N##R##N##R##N#Originality/value#R##N##R##N##R##N##R##N##R##N#Many approaches estimate user interest based on the terms in tets and apply such graph theory as following networks. The authors propose a new estimation method that uses the time series data of the number of tets. The merits to estimating user interest using the time series data do not depend on language and can decrease the calculation costs compared with the above-mentioned approaches because the number of features is fer."}]