Now-a-days, people in general choose to purchase an online item depending on the reviews and feedback given in social media. The probability of leaving a scrutiny gives a glorious chance for person who writing a spam reviews regarding the product for individual reasons. Categorizing these kinds of spammers and the spam content create an interesting issue of analysis. Though a generous range of studies are done recently towards this, till date the methodologies used still hardly find the spam reviews. Here, we propose an exclusive framework called Net-Spam that exploit spam options for creating review datasets as heterogeneous data networks to map spam detection procedure into further classifications. Maltreatment of spam options helps us to get the best output pertaining to various metrics experimented on real-time review datasets from Amazon and Yelp websites. The results show that Net-Spam outperforms the current ways from among the three classes of features namely detection of review, detection of user and detection of Spam Groupers.