The social Web provides opportunities for the public to have social interactions and online discussions. A large number of online users using the social web sites create a high volume of data. This leads to the emergence of Big Data, which focuses on computational analysis of data to reveal patterns, and associations relating to human interactions. Such analyses have vast applications in various fields such as understanding human behaviors, studying culture influence, and promoting online marketing. The blogs are one of the social web channels that offer a way to discuss various topics. Finding the top bloggers has been a major research problem in the research domain of the social web and big data. Various models and metrics have been proposed to find important blog users in the blogosphere community. In this work, first find the sentiment of blog posts, then we find the active and influential bloggers. Then, we compute various measures to explore the correlation between the sentiment and active as well as bloggers who have impact on other bloggers in online communities. Data computed using the real world blog data reveal that the sentiment is an important factor and should be considered as a feature for finding top bloggers. Sentiment analysis helps to understand how it affects human behaviors.