Abstract
With the growth in the communication systems, opinions became the most used communication method in the corporates, research and education. Nevertheless, with the increasing popularity the challenge for all internet service providers is to keep matching the demand for bandwidth. The major challenge to keep the bandwidth up to the usage is dealing with the spam messages. A spam communication or review is something that the sender uses for promotion and for the received may be useless. Thus for the receiver the messages are mostly unimportant. The detection of the spam reviews cannot be done at the review server end and need to done at the receiver side. Failing in detecting the spam can easily overload the review communication channel and reduce the effective use of the bandwidth. A number of researchers are carried out in order to detect the spam messages by deploying the filters. The outcomes are partially satisfactory as most of the parallel researches have demonstrated the rejection of the documents based on the pre-defined keywords. Nonetheless, these methods are not satisfactory as the use of words for every review writer may vary. As a result influenced by certain keywords, the receiver may lose some important communications. Thus the demand of the modern research is to enhance the detection of the spam reviews by using enhanced techniques rather than only depending on the keywords. This work proposes a novel automated framework powered by machine learning technique to detect the keywords and improve the detection by deploying context detection methods. The major outcome of this work is to build and demonstrate an automated framework for review spam detection with review rejection filters. The work outcomes into a highly satisfactory detection rate and demonstrate a sustainable model.
Highlights
The notable review carried out by Yenuga Padma et al [1] has demonstrated the types of the spam and barriers caused by those types
Positive reviews about products generally results in a purchase of a product and vice-versa
Behavioural analysis and supervised methods are used by many researchers to address the Yenuga Padma et al, International Journal of Advanced Research in Computer Science, 9 (1), Jan-Feb 2018,587-591 problem opinion spam detection
Summary
The notable review carried out by Yenuga Padma et al [1] has demonstrated the types of the spam and barriers caused by those types. This review lays the demand for an automatic framework for opinion mining and detection of spam information specially the reviews. People use web for everything, they use web to solve their questions, to find solutions of unsolved problems, to know about not so known products or services etc They use web, to know opinions of others before finalizing their decision on purchase of a new product or service. Positive reviews about products generally results in a purchase of a product and vice-versa This reveals that opinions influence decision making of individuals and organizations. Jindal et al [2] has demonstrated significant outcomes by deploying adaptive process for detection of spam reviews and information on the web. The work by SihongXie et al [3] has demonstrates a similar approach for the detection method.
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