Abstract: The increasable usage of social media in expressing opinions has raised the importance of Social Network Analysis (SNA). Business owners utilize SNA to detect influence users who can motivate others to buy their products through growing positive feedback. This emphasizes the need to consider people’s perspectives in the process of Sentiment Analysis (SA). Considering perspectivism while computing text polarity can help the machine to reflect the human perceived sentiment within text content. Moreover, text vagueness still distresses the accuracy of SA. In this study, a fuzzy-based SA approach for Twitter is proposed that handles perspectivism through integrating SNA with the sentiment process. SA is done using Text Blob and Fuzzy logic while SNA is done using UCINET tool and Artificial Neural Networks (ANN) to rank users. This research aims to avoid misleading sentiments, improve sentiment classification accuracy, and deal with social behaviors. After all, a more real sentiment is produced that reflects what readers have perceived. The fuzzy classification technique was adopted to deal with the vagueness of language and for finegrained classification of Tweets into seven classes instead of the binary classification. A comparative analysis between Type-1 and Type-2 fuzzy logic is conducted to choose the technique with better performance. The proposed model is practiced on data collected from Twitter. Results show significance in the use of Type-2 fuzzy logic in terms of model accuracy with the ability to handle perspectivism.