Abstract: Truth discovery aims at obtaining the most credible information from multiple sources that provide noisy and conflicting values. Identifying trustworthy information in the presence of noisy data has been a crucial task. The most important challenge is to identify “misinformation spread” where a significant number of sources are contributing to false claims, making the identification of truthful claims difficult. Reviews given for the particular event can be false and mislead new users, so to avoid that we use two methods to identify the truthfulness of the review; One TPR (True Positive Rate) and second FPR (False Positive Rate). We also use RRA (Reporting Rate if Absent) to check the reporting rate of the user for that particular event. We use Crowd Analysis algorithm to calculate the above mentioned TPR, FPR and RRA. We use User Rate Summarization algorithm for checking if the user is with the crowd or without the crowd. We use Naïve Bayes algorithm for Sentiment Analysis of the reviews. The evaluation results on realworld datasets show that this method significantly outperforms the state-of-the-art truth discovery methods in terms of both effectiveness and efficiency. Keywords: Naïve Bayes algorithm, TPR, FPR, RRA, Truth Discovery.