Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.