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Online Reviews Regarding Different Products Or Services Have Become The Main Source To Determine Public Opinions. Unfortunately, To Gain Profits Or Fame, Spam Reviews Are Written To Promote Or Demote Targeted Products Or Services. This Practice Is Known As Review Spamming. This Can Help To Analyze The Impact Of Widespread Opinion Spam In Online Reviews. In This Work, Two Different Spam Review Detection Methods Have Been Proposed: (1) Spam Review Detection Using Behavioral Method (SRD-BM) Utilizes Thirteen Different Spammer’s Behavioral Features To Calculate The Review Spam Score Which Is Then Used To Identify Spammers And Spam Reviews, And (2) Spam Review Detection Using Linguistic Method (SRD-LM) Works On The Content Of The Reviews And Utilizes Transformation, Feature Selection And Classification To Identify The Spam Reviews. Experimental Evaluations Are Conducted On A Real-world Amazon Review Dataset Which Analyze 26.7 Million Reviews And 15.4 Million Reviewers. The Evaluations Show That Both Proposed Models Have Significantly Improved The Detection Process Of Spam Reviews. Comparatively, SRD-BM Achieved Better Accuracy Because It Works On Utilizing Rich Set Of Spammers Behavioral Features Of Review Dataset Which Provides In-depth Analysis Of Spammer Behavior. To The Best Of Our Knowledge, This Is The First Study Of Its Kind Which Uses Large-scale Review Dataset To Analyze Different Spammers’ Behavioral Features And Linguistic Method Utilizing Different Available Classifiers.