Social networks analysis studies the interactions among users when using social media. The content provided by social media is composed of essentially two parts: a network structure of users’ links (e.g. followers, friends, etc.) and actual data content exchanged among users (e.g. text, multimedia). Topic modeling and sentiment analysis are two techniques that help extracting meaningful information from large or multiple portions of the text: identifying the topic discussed in a text, and providing a value characterizing an opinion respectively. This extracted information can then be combined to the network structure of users’ links for further tasks as predictive analytics, pattern recognition, etc. In this paper we propose a method based on graph databases, topic modelling and sentiment analysis to facilitate pattern extraction within social media texts. We applied our model to Twitter datasets, and were able to extract a series of opinion patterns.