The rapid research on the traditional recommender systems has proved the usefulness of the decision support tools on various real-time applications. In the recent years, hybrid recommendation models have become more popular due to its increased efficiency to manage the information overload problem. The context-aware location recommendations based on user's emotions improves the user satisfaction levels, but still the emotion based recommendation models are not explored completely due to the real-time issues in the acquisition of the user's emotions. This article presents an effective recommendation model for the location recommendation through exploiting the emotion of the user from online social media. In the proposed model, User, Point-of-Interest and User's Emotion during travel are the three main factors taken into consideration to generate recommendations. The proposed location recommendation models correlate the positive and negative impact of the user's emotions to generate the list of user relevant locations. The developed models are evaluated on the large-scale real world datasets and obtained results were compared with the existing baseline models. The presented results prove the improved efficiency and accuracy of the proposed location recommender system through validation by standard evaluation metrics.