Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fake Reviews Detection through Ensemble Learning   Customers represent their satisfactions of consuming products by sharing
their experiences through the utilization of online reviews. Several machine
learning-based approaches can automatically detect deceptive and fake reviews.
Recently, there have been studies reporting the performance of ensemble
learning-based approaches in comparison to conventional machine learning
techniques. Motivated by the recent trends in ensemble learning, this paper
evaluates the performance of ensemble learning-based approaches to identify
bogus online information. The application of a number of ensemble
learning-based approaches to a collection of fake restaurant reviews that we
developed show that these ensemble learning-based approaches detect deceptive
information better than conventional machine learning algorithms.

11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph