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      Slang feature extraction by analysing topic change on social media

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          Abstract

          Recently, the authors often see words such as youth slang, neologism and Internet slang on social networking sites (SNSs) that are not registered on dictionaries. Since the documents posted to SNSs include a lot of fresh information, they are thought to be useful for collecting information. It is important to analyse these words (hereinafter referred to as ‘slang’) and capture their features for the improvement of the accuracy of automatic information collection. This study aims to analyse what features can be observed in slang by focusing on the topic. They construct topic models from document groups including target slang on Twitter by latent Dirichlet allocation. With the models, they chronologically the analyse change of topics during a certain period of time to find out the difference in the features between slang and general words. Then, they propose a slang classification method based on the change of features.

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          Most cited references 19

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          topicmodels: AnRPackage for Fitting Topic Models

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            Topic detection using paragraph vectors to support active learning in systematic reviews

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              Representation learning for very short texts using weighted word embedding aggregation

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                Author and article information

                Contributors
                Journal
                TRIT
                CAAI Transactions on Intelligence Technology
                CAAI Trans. Intell. Technol.
                The Institution of Engineering and Technology
                2468-2322
                March 2019
                18 January 2019
                4 February 2019
                : 4
                : 1
                : 64-71
                Affiliations
                Graduate School of Technology, Industrial and Social Sciences, Tokushima University , 770-8506, Tokushima-shi, Minamijosanjima-cho 2-1, Japan
                Article
                TRIT.2018.1060 CIT.2018.1060.R1
                10.1049/trit.2018.1060

                This is an open access article published by the IET, Chinese Association for Artificial Intelligence and Chongqing University of Technology under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

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                Funding
                Funded by: JSPS KAKENHI
                Award ID: JP15K16077, JP15H01712
                Categories
                Research Article

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