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Comparison of attributes for predicting online reviews on Amazon

Angelina Temelkovska (2016) Comparison of attributes for predicting online reviews on Amazon. EngD thesis.

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    On every day basis, we grade and make comments about many subjects around us. This thesis is aiming to show how can we predict the grades on the largest online retailer nowadays Amazon. For that purpose, we built models in three different phases, by three different but also closely connected fields in the data analysis branch. At the beginning, we give a short overview of each field and basic mathematical description of the models and estimators we use. Via those models, we show the big picture of which review's attributes give us the most information about user's numerical score. Each of the three approaches extracts various attributes such as the time stamp, the helpfulness, the words in the comments or the interaction between the users, to name a few. Furthermore, we explore those features by comparing different combinations of them at each of the three steps. Then, we evaluate their success in making a prediction of the numerical score in each review. At the end, we conclude with some of the advantages and disadvantages of the built models and possibilities for future improvements and further work.

    Item Type: Thesis (EngD thesis)
    Keywords: machine learning, data mining, text mining, network analysis, movies, scores, comments, users
    Number of Pages: 68
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    doc. dr. Lovro ŠubeljMentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537245123)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3634
    Date Deposited: 05 Oct 2016 09:22
    Last Modified: 27 Oct 2016 11:12
    URI: http://eprints.fri.uni-lj.si/id/eprint/3634

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