Published Paper Details:

With the impulsive growth of social media on the Web, individuals and organizations are more and more using public opinions for their decision making. The opinionated text retrieved from protracted forum postings, discussion rooms, chats and blogs. It is difficult for the average human reader identifying relevant posts and summarizing the information accurately. Moreover, it is human pschycology that people often pay superior attention to opinions that are harmonizing with their own preferences. As there are ample of sites available, extracting the factual information is a challenging task, rather than, finding relevant information on the Web. Keeping in the mind, the easy accessibility of the reviews and the significant impacts in the digital market, there is an increasing support to manipulate the reviews, mostly profit driven. In near future, spam reviews will damage the entire online review systems and finally cause a gradual loss of credibility. Hence, the first step towards securing the online review system is detecting the spam reviews.There are no clear hints or signs from the text of the reviews that it is real or fake. Web Mining and Machine learning techniques both put forward an inspiring contribution to detecting fraudulent reviews The key objective of this paper is to provide a strong and comprehensive analysis on current research and detecting review spam machine learning techniques.