Recent innovations of the Internet-of-Things (IoT) are facilitated the data processing to an enormous scale. Nevertheless, the collected data are inherently uncertain because of noise, discrepancy, and incompleteness. To solve these constraints, improving data veracity is crucial for IoT-based applications. As big data veracity is more critical, it must predict the veracity of tweets automatically for decision-making. There have been many techniques developed including artificial intelligence and crowdsourcing models to estimate the veracity of tweets from single sources. But, these models are ineffective while the amount of crowd labels for tweets from multiple sources is not feasible. Also, analyzing the sentiment of public opinions has high complexity. Therefore, this article focuses on developing an efficient model to predict the veracity of tweets from multiple sources and analyze the sentiments of the passage. Primarily, different tweets with text labels are collected. Afterward, a crowdsourcing model is introduced which utilizes the crowdflower dataset to obtain the crowd labels for collected tweets. The Generative Adversarial Network (GAN) model is applied to increase the veracity of tweets by handling the low-veracity tweets. Finally, the multi-set feature learning model is implemented to classify the sentiment of opinions. The performance results manifest that the proposed model accomplishes superior accuracy of 97% than the classical veracity prediction models.