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    Twitter Sentiment Mining: A Multi Domain Analysis

    192950_192950.pdf (355.0Kb)
    Access Status
    Open access
    Authors
    Shahheidari, S.
    Dong, Hai
    Bin Daud, M.N.R.
    Date
    2013
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Shahheidari, Saeideh and Dong, Hai and Bin Daud, M.N.R. 2013. Twitter Sentiment Mining: A Multi Domain Analysis, in Barolli, L. and Xhafa, F. and Chen, H.C. and Gómez-Skarmeta, A.F. and Hussain, F. (ed), Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), Jul 3-5 2013, pp. 144-149. Asia University, Taichung, Taiwan: IEEE.
    Source Title
    2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems
    Source Conference
    CISIS 2013
    DOI
    10.1109/CISIS.2013.31
    ISBN
    978-0-7695-4992-7
    Remarks

    Copyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/34303
    Collection
    • Curtin Research Publications
    Abstract

    Microblogging such as Twitter provides a rich source of information about products, personalities, and trends, etc. We proposed a simple methodology for analyzing sentiment of users in Twitter. First, we automatically collected Twitter corpus in positive and negative tweets. Second, we built a simple sentiment classifier by utilizing the Naive Bayes model to determine the positive and negative sentiment of a tweet. Third, we tested the classifier against a collection of users’ opinions from five interesting domains of Twitter, i.e., news, finance, job, movies, and sport. The experimental results show that it is feasible to use Twitter corpus alone to classify new tweet for a certain domain applications.

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