In this paper, we present a lexicon-based sentiment analysis method that is used as an annotation scheme for identifying fine-grained emotions in social media topics. This methodology is based on Plutchik's wheel of emotion and Latent Dirichlet Allocation (LDA). We firstly annotate a tweet based on eight basic emotions and secondly we compute further eight dyads as a product of the basic emotions. We demonstrate that this lexicon-based approach achieves up to 78.53% ground truth accuracy when compared to human annotated data that is split into positive and negative polarities. Moreover, we investigate a novel means to identify trending topics in twitter data by utilizing LDA and focusing on fine-grained emotions associated with each tweet. We compare the most dominant emotions in social media as topics from an emotion-document pooling strategy and compare the results to an author-topic modeling strategy.