Objective: Identifying influential users in online communities is important in the era of social networking. For example it is extremely helpful to promote product/campaign or immunize rumors among its members. Method: In this paper we propose a hybrid approach where influential rank of a user is calculated using a Sentiment Weighted Page Ranking Algorithm (SWPR). The core logic behind this methodology is, any interaction between two nodes is taken into consideration and its associated sentiment is calculated. Then it considers degree centralities for general rank calculation and the sentiment associated of a user is fed as its 'weight'. Findings: After experimenting our proposed methodology with 532 nodes and tracing the data, it's inferred that infection spread by top 10 users ranked by SWPR is higher and faster than page rank influential users. The newly proposed ranking methodology exceed spreading rate when compared with other traditional network metrics and other ranking methods. Further interpreting the data, we also see that infection rate varies based on context of the data. Mostly it was sentiment driven and for few cases it was context driven with mild effect of underlying sentiment. The experimental results show that, considering users associated sentiment as weight, gives much more accuracy than traditional ranking methods. Improvement: Our proposed algorithm performs better by identifying influential users more accurately than other methods. As the accuracy improved, the campaign and product promotions reached faster to desired members.