A Review on Spam Detection Based on Machine Learning Techniques
Keywords:
Big data, Classification, Machine learning, Opinion mining, Review spam, Web miningAbstract
Reviews found on the internet are a great way to find out how the general public feels about a product or service before buying it, and they are also the single most influential element for customers when making a purchase choice. Manufacturers and retailers pay great attention to client comments and feedback due to the influence they have. Concerns about dishonest people using their review platforms to artificially boost or lower the value of products and services are understandable, given the prevalence of online review platforms. Spammers engage in this activity when they manipulate and poison comments for financial gain by making fake, misleading, or otherwise deceptive comments. It is critical to find ways to identify spam because not all evaluations on the internet are genuine. Several machine learning algorithms can be employed for spam identification by utilizing natural language processing (NLP) to derive useful functions from text. Along with the content itself, this approach can also benefit from proofreading information. This article explores the performance of many methods for categorizing and identifying spam comments, as well as the outstanding machine learning strategies offered to address the problem of comment spam detection.
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