IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v15y2017i3d10.1007_s10257-016-0320-0.html
   My bibliography  Save this article

URWF: user reputation based weightage framework for twitter micropost classification

Author

Listed:
  • Asad Bukhari

    (National University of Sciences and Technology)

  • Usman Qamar

    (National University of Sciences and Technology)

  • Ume Ghazia

    (National University of Sciences and Technology)

Abstract

Sentiment analysis is an emerging field that helps in understanding the sentiments of users on microblogging sites. Many sentiment analysis techniques have been proposed by researchers that classify and analyze the sentiments from micropost posted by various users. Majorly, these techniques perform text based classification that does not allow predicting the micropost impact. Further, it is very difficult to analyze this huge volume of online content produced each day. Therefore, an effective technique for sentiment analysis is required that not only performs the precise text-based classification but also makes the analysis easy by reducing the volume of data. Moreover, micropost impact must also be determined in order to segregate the high impact microposts in corpus. In the present study, we have presented sentiment analysis framework that incorporates any text based classification and separates out the high impact microposts from low impact by calculating the factor of user reputation. This user reputation is calculated by considering multiple factors regarding user activities that may help organizations to know customer opinions and views about their products and services. This way, volume of data becomes small that has to be analyzed by considering only microposts posted by high impact users. Multiple text classifications classes are introduced instead of just positive, negative and neutral for precise sentiment classification. The proposed framework also calculates the accumulated weight of each micropost by multiplying the user reputation with the assigned sentiment score. The user reputation calculation factors are validated by using Spearman rho and Kendall tau correlation coefficient. The framework is further evaluated by using the Sanders topic based corpus and results are presented.

Suggested Citation

  • Asad Bukhari & Usman Qamar & Ume Ghazia, 2017. "URWF: user reputation based weightage framework for twitter micropost classification," Information Systems and e-Business Management, Springer, vol. 15(3), pages 623-659, August.
    • Handle: RePEc:spr:infsem:v:15:y:2017:i:3:d:10.1007_s10257-016-0320-0
    DOI: 10.1007/s10257-016-0320-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-016-0320-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10257-016-0320-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
    2. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
    3. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    4. Asad Bukhari & Usman Qamar & Ume Ghazia, 0. "URWF: user reputation based weightage framework for twitter micropost classification," Information Systems and e-Business Management, Springer, vol. 0, pages 1-37.
    5. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    6. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    7. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    8. Zaili Yang & Esin Erol Mehmed, 2019. "Artificial neural networks in freight rate forecasting," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 390-414, September.
    9. Hakob GRIGORYAN, 2015. "Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(2), pages 14-23, October.
    10. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    11. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    12. Luna, Ivette & Ballini, Rosangela, 2011. "Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 708-724, July.
    13. Tea Šestanović & Josip Arnerić, 2021. "Neural network structure identification in inflation forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 62-79, January.
    14. Sutapa Chaudhuri & Arumita Roy Chowdhury, 2018. "Air quality index assessment prelude to mitigate environmental hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 1-17, March.
    15. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
    16. Fhulufhelo Walter Mugware & Caston Sigauke & Thakhani Ravele, 2024. "Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions," Forecasting, MDPI, vol. 6(3), pages 1-28, August.
    17. Jordan French, 2016. "Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets," IJFS, MDPI, vol. 4(3), pages 1-13, July.
    18. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
    19. Fabin Shi & Xiao-Qian Sun & Jinhua Gao & Zidong Wang & Hua-Wei Shen & Xue-Qi Cheng, 2021. "The prediction of fluctuation in the order-driven financial market," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infsem:v:15:y:2017:i:3:d:10.1007_s10257-016-0320-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.