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Taghi M. Khoshgoftaar1, To the best of our knowledge, methods and tools for learning from Big Data have not been used in the literature even though real world datasets of only a single site (such as Trip Advisor) can contain upwards of 200 million reviews5. New reviews are constantly being added to large repositories of reviews across various websites at a high rate, over 1.5 million per month in the case of Yelp4. Consequently, distributed and streaming applications of machine learning algorithms across these datasets are of interest as traditional machine learning tools, such as R or Weka, cannot scale to datasets of this size. Tools such as MahoutFootnote 11, Spark (MLlib)Footnote 12, H2OFootnote 13, and SAMOAFootnote 14 should be explored to effectively model the large corpus of online reviews which exist in the real world [38]. Mahout has been used for large-scale recommendation systems [39], which would be useful to apply to review spam detection, as reviewers may be related to each other on different review websites. MLlib and SAMOA can perform large-scale online learning, where machine learning models are trained and tuned as new data flows in. This is especially desirable in the field of review spam detection, as reviews are constantly being added to the corpus. SAMOA has been used to analyze live Twitter streams [40], which involves similar text processing that can be applied to online reviews. you have been. The cash money and will go. If they're for your savings in a $4 of the Start Free Trial Avani Gregg $4.75 million how to make money writing children s books on amazonmake money leaving amazon reviews
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