The explosive growth of social media networks is one of the clearest trends of the past decade and a half. The number of active users on Facebook has risen from one million in 2004 to nearly two billion in mid-2017. In the political realm, Donald Trump publicly credited his victory in the 2016 presidential election to his effective se of Twitter and Facebook. These media are the subject of studies originating n various research fields, such as social network analysis (SNA), natural language processing (NLP), data mining, etc. Issues worthy of study include cascades in he network (i.e. shapes of re-tweets), information diffusion, influencer detection, recommendation systems and opinionmining. The framework uses topic modelling to classify content by topic or theme, sentiment analysis to extract opinions from he content. Based on the topic classification, we isolate network fragments of a user and his followees to track the opinion changes of this particular group. We also provide a generic graph database model that stores the follower graph, the metadata and content. We set criteria to define the notion of a discussion and to identify discussion patterns. The proposed definition of a discussion is composed of a series of statistical values and charts applied to each network fragment. We propose a method to extract discussion patterns and a method to compare and extract patterns from within social network datasets. This thesis presents an instantiation of the framework of opinion change for Twitter. We evaluated our framework using two datasets crawled on Twitter, a mixed dataset (crawled from April 16, 2016 to April 21, 2016) and a political dataset (crawled from November 28, 2016 to December 08, 2016). Preliminary results show that the political dataset contained fewer opinion changes, was more polarized and provided a less dense network. The mixed dataset provided a dense network with many opinion changes, but many of its posts had a neutral opinion and users contributed a lower volume of content. Such opinion change analysis can be useful for studying customer behavior for marketing purposes, for studying voter behavior for electoral purposes, for enriching recommendation tools and for studying information diffusion inside social networks.