Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings

Research Article

Integrated Sentiment and Emotion into Estimating the Similarity Among Entries on Social Network

Download
94 downloads
  • @INPROCEEDINGS{10.1007/978-3-319-74176-5_21,
        author={Thi Nguyen and Dinh Tran and Gia Dam and Manh Nguyen},
        title={Integrated Sentiment and Emotion into Estimating the Similarity Among Entries on Social Network},
        proceedings={Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings},
        proceedings_a={INISCOM},
        year={2018},
        month={1},
        keywords={Similar measure Social network Text Entry Social media},
        doi={10.1007/978-3-319-74176-5_21}
    }
    
  • Thi Nguyen
    Dinh Tran
    Gia Dam
    Manh Nguyen
    Year: 2018
    Integrated Sentiment and Emotion into Estimating the Similarity Among Entries on Social Network
    INISCOM
    Springer
    DOI: 10.1007/978-3-319-74176-5_21
Thi Nguyen1,*, Dinh Tran2,*, Gia Dam1,*, Manh Nguyen,*
  • 1: Vietnam Commercial University
  • 2: Posts and Telecommunications Institute of Technology (PTIT)
*Contact email: hoint2002@gmail.com, tdque@yahoo.com, damgiamanh@gmail.com, nmh.nguyenmanhhung@gmail.com

Abstract

Similar measures play an important role in information processing and have been widely investigated in computer science. With the exploration of social media such as Youtube, Wikipedia, Facebook etc., a huge number of entries have been posted on these portals. They are often described by means of short text or sets of words. Discovering similar entries based on such texts has become challenges in constructing information searching or filtering engines and attracted several research interests. In this paper, we firstly introduce a model of entries posted on media or entertainment portals, which is based on their features composed of title, category, tags, and content. Then, we present a novel similar measure among entries that incorporates their features. The experimental results show the superiority of our incorporation similarity measure compared with the other ones.