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    Identifying Mubasher software products through sentiment analysis of Arabic tweets

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    Authors
    AL-Rubaiee, Hamed Saad
    Qiu, Renxi
    Li, Dayou
    Affiliation
    University of Bedfordshire
    Issue Date
    2016-05-02
    Subjects
    Saudi Arabia
    pre-processing
    data mining
    text mining
    sentiment analysis
    Mubasher
    Twitter
    Arabic
    
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    Abstract
    Social media has recently become a rich resource in mining user sentiments. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. Firstly, document's Pre-processing are explored on the dataset. Secondly, Naive Bayes and Support Vector Machines (SVMs) are applied with different feature selection schemes like TF-IDF (Term Frequency-Inverse Document Frequency) and BTO (Binary-Term Occurrence). Thirdly, the proposed model for sentiment analysis is expanded to obtain the results for N-Grams term of tokens. Finally, human has labelled the data and this may involve some mistakes in the labelling process. At this moment, neutral class with generalisation of our classification will take results to different classification accuracy.
    Citation
    Al-Rubaiee H, Qiu R, Li D (2016) 'Identifying Mubasher software products through sentiment analysis of Arabic tweets', International Conference on Industrial Informatics and Computer Systems (CIICS) - Sharjah, Institute of Electrical and Electronics Engineers Inc..
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    URI
    http://hdl.handle.net/10547/623853
    DOI
    10.1109/ICCSII.2016.7462396
    Additional Links
    https://ieeexplore.ieee.org/abstract/document/7462396
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781467387439
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCSII.2016.7462396
    Scopus Count
    Collections
    Computing

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