The rapidly increasing of sentiment analysis in social networks has lead business owners and decision makers to value opinion leaders who can influence people's impressions concerning certain business or commodity. Nevertheless, decision makers are being misled by inaccurate results due to the ignorance of perspectivism. Considering perspectivism, while computing text polarity, can help machines to reflect the human perceived sentiment within the content. This emphasises the need for integrating social behaviour (user's influence factor) with sentiment analysis (text polarity scores), providing a more pragmatic portrayal of how the writer's audience comprehend the message. In this study, a new model is proposed to intensify sentiment analysis process on Twitter. In the achievement of such, social network analysis is done using UCINET tool followed by artificial neural networks for ranking users. For sentiment classification, a hybrid approach is presented, where lexicon-based technique is combined with a fuzzy classification technique to handle language vagueness as well as for an inclusive analysis of tweets into seven classes; for the purpose of enhancing final results. The proposed model is practiced on data collected from Twitter. Results show a significant enhancement in tweets polarity scores represent more realistic sentiments.